20th August, 2026
Deadline
LOCATION:
New Delhi
New Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
REGISTRATIONS OPEN
Apply by August 20th
Indian Army
Terrier Cyber Quest 2026
75 Years of Territorial Army
A national stage for cyber security, artificial intelligence and frontier defence technology. Building on the tremendous success of Terrier Cyber Quest 1.0 and 2.0 the Indian Army Terrier Cyber Quest 3.0 aims to delve deeper into fostering innovation and collaboration in critical domains like securing emerging technologies. With an expanded scope and challenging tracks, this phase continues the journey to unite India's brightest minds from academia, industry, and government to address modern defense challenges through technology.
August 20th, 2026
Deadline
EVENT LOCATION
LOCATION: Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
Participants may register in the following 3 Tracks of the Indian Army Terrier Cyber Quest 2026:-
Bughunting
Hunt the flaw, opportunity to challenge state-of-the-art government systems.
Competition Format
Compete in an online Capture-The-Flag (CTF) challenge during the shortlisting process. The shortlisted teams shall compete in a 36-hour live Indian Army-specific customized solution finale (in a simulated national-infrastructure sandbox). The shortlisted teams shall detect vulnerabilities, and document the exploits of the vulnerabilities detected. Participants shall also share links to their certifications or badges earned in the cybersecurity domain.
Participation guidelines:
Who: Students, Cybersecurity Professionals, Ethical Hackers, Armed Force Personnels & Researchers.
Format: Fully online CTF qualifier & in-person grand finale; team of maximum 3 members (including team leader).
Scoring: An expert panel would grade the submissions made during the grand finale, based on severity of vulnerabilities detected, methodology & complexity.
Who: Students, Cybersecurity Professionals, Ethical Hackers, Armed Force Personnels & Researchers.
Format: Fully online CTF qualifier & in-person grand finale; team of maximum 3 members (including team leader).
Scoring: An expert panel would grade the submissions made during the grand finale, based on severity of vulnerabilities detected, methodology & complexity.
Grand Finale
The top 10 shortlisted teams shall compete in the Grand Finale which will be a 36-hour in-person competition to be held in New Delhi in a secure, simulated national infrastructure environment. Participants shall perform live vulnerability discovery and documentation on a customized system stack with realistic dummy data. Bugs are evaluated based on severity, originality, and complexity, with an expert panel verifying findings and determining the final scores. The top 3 teams shall be declared winners based on their overall performance.
The top 10 shortlisted teams shall compete in the Grand Finale which will be a 36-hour in-person competition to be held in New Delhi in a secure, simulated national infrastructure environment. Participants shall perform live vulnerability discovery and documentation on a customized system stack with realistic dummy data. Bugs are evaluated based on severity, originality, and complexity, with an expert panel verifying findings and determining the final scores. The top 3 teams shall be declared winners based on their overall performance.
Timeline
Registrations
July 30th , 2026 (Thursday) – August 20th , 2026 (Thursday)
Shortlisting Phase
September 1st, 2026 (Tuesday) - September 10th, 2026 (Thursday)
Grand Finale
October 6th, 2026 (Tuesday) – October 8th, 2026 (Thursday)
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Award Ceremony:
October 9th, 2026 (Friday)
AI Kavach
Kavach means shield: defensive by design
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Competition Format:
The AI Kavach is a data-centric innovative challenge designed to test participants' ability to build robust technological solutions/ models that can detect, analyse and solve real-world problems related to defence and national security.
Build a cyber-reasoning system - an LLM laced with fuzzers, static and dynamic analysis, and a regression test harness - that autonomously finds a vulnerability, patches it, and proves the fix holds. The solutions worked out in the finale by the teams shall be pitched to run autonomously against specific customised infrastructure of the Indian Armed Forces.
The AI Kavach is a data-centric innovative challenge designed to test participants' ability to build robust technological solutions/ models that can detect, analyse and solve real-world problems related to defence and national security.
Build a cyber-reasoning system - an LLM laced with fuzzers, static and dynamic analysis, and a regression test harness - that autonomously finds a vulnerability, patches it, and proves the fix holds. The solutions worked out in the finale by the teams shall be pitched to run autonomously against specific customised infrastructure of the Indian Armed Forces.
Participation guidelines:
Who: Students, AI/ ML Professionals, Security Researchers, Armed Forces Personnel, Startups & Industry Professionals.
Entry: Teams of maximum 3 members (including team-leader).
Submission: Participants are required to submit a PowerPoint presentation (PPT) consisting of not more than 5 slides that clearly explains their proposed solution(including the tech stack). The presentation should be prepared in the following structure:
Slide 1-Introduction, Ideation & Brief Description: problem statement overview, proposed idea and motivation, brief description of the solution.
Slide 2- Detailed Methodology: step-by-step approach for solving the challenge, implementation strategy and workflow.
Slide 3- Technology Stack / Flow Diagram / Block Diagram / Equipment Used: technologies, frameworks, and tools to be used, system architecture, flowchart, or block diagram, hardware/equipment (if applicable).
Slide 4- Salient Features & Novelty of the Proposed Project: key features of the solution, innovation, uniqueness, and advantages over existing approaches, the USP should be clearly highlighted.
Slide 5- Final Deliverables: expected outcomes and project deliverables, performance objectives, prototype, software, model, or demonstration (as applicable), Proof-of-concept to be submitted by the participants.
Scoring: Evaluation for short listing shall be done by expert jury based on resource utilisation, novelty of idea, how light-weight the solution is.
Entry: Teams of maximum 3 members (including team-leader).
Submission: Participants are required to submit a PowerPoint presentation (PPT) consisting of not more than 5 slides that clearly explains their proposed solution(including the tech stack). The presentation should be prepared in the following structure:
Slide 1-Introduction, Ideation & Brief Description: problem statement overview, proposed idea and motivation, brief description of the solution.
Slide 2- Detailed Methodology: step-by-step approach for solving the challenge, implementation strategy and workflow.
Slide 3- Technology Stack / Flow Diagram / Block Diagram / Equipment Used: technologies, frameworks, and tools to be used, system architecture, flowchart, or block diagram, hardware/equipment (if applicable).
Slide 4- Salient Features & Novelty of the Proposed Project: key features of the solution, innovation, uniqueness, and advantages over existing approaches, the USP should be clearly highlighted.
Slide 5- Final Deliverables: expected outcomes and project deliverables, performance objectives, prototype, software, model, or demonstration (as applicable), Proof-of-concept to be submitted by the participants.
Scoring: Evaluation for short listing shall be done by expert jury based on resource utilisation, novelty of idea, how light-weight the solution is.
Grand Finale:
During the in person 36-hour Grand Finale, shortlisted teams will build and refine their proposed solutions into working prototypes with support from mentors and provided resources. Solutions will be evaluated on performance, speed, precision, functionality, and scalability.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
The final prototypes will also be tested against a simulated Indian Armed Forces software environment to assess their effectiveness in realistic operational scenarios.
Timeline
Registrations
July 30th , 2026 (Thursday) – August 20th , 2026 (Thursday)
Shortlisting Phase
September 1st, 2026 (Tuesday) - September 10th, 2026 (Thursday)
Grand Finale
October 6th, 2026 (Tuesday) – October 8th, 2026 (Thursday)
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Award Ceremony:
October 9th, 2026 (Friday)
Creators
Challenge
Challenge
National Creators Challenge
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Brief and Registration
Tell it your way – do not make a deepfake to unmask the trick! Make a short, original 1-3 minute reel / video or carousel that creates awareness and alerts your audience about popular deepfakes or cyber manipulation techniques being deployed on digital platforms to decieve the public at large. A national challenge organised for creators, influencers and netizens to help the nation to see through deepfakes and AI-generated disinformation through the educational / awareness generating a reel / video on how to recognise the manipulation, and to prevent it's further propagation.
Participation guidelines:
Who: Creators, Influencers, Students & Netizens
Themes: Emotional manipulation, Impersonation, Manufactured outrage, Think-before-you-forward
Format: Teams of maximum 4 members
Scoring: Creativity, novelty, accuracy, clarity, thematic-relevance & coherence.
Who: Creators, Influencers, Students & Netizens
Themes: Emotional manipulation, Impersonation, Manufactured outrage, Think-before-you-forward
Format: Teams of maximum 4 members
Scoring: Creativity, novelty, accuracy, clarity, thematic-relevance & coherence.
Shortlisting and Rules:
Original Work: Entries must be the original work of the participant and must not infringe any third-party intellectual property or other legal rights. Plagiarised or infringing entries will be disqualified.
AI-Generated Content: Synthetic or AI-generated media depicting real, identifiable individuals is prohibited.
Content Standards: Entries must abide by the law of the land, suitable for all audiences, and must not contain defamatory, obscene, hateful, misleading, or partisan political-party content.
Minors: Participants under 18 years of age must submit their reel / video along with a verifiable consent of submission from his / her parent or legal guardian.
Shortlisting: Submission of an entry does not create any obligation on the Organisers to shortlist the entry. The Organisers reserve the right to verify eligibility and authenticity and to reject or disqualify any entry at their sole discretion.
Detailed Agreement: Shortlisted participants shall, as a condition of further participation, execute a detailed Participation Agreement covering, inter alia, confidentiality (NDA), intellectual property rights, licensing and usage rights, media permissions, representations and warranties, indemnities, code of conduct, eligibility requirements, and such other terms as the Organisers may prescribe. Failure to execute such agreement within the stipulated timeline may result in disqualification.
Final Decision: All decisions of the Organisers regarding eligibility, shortlisting, evaluation, and the competition shall be final and binding upon all the participating teams.
AI-Generated Content: Synthetic or AI-generated media depicting real, identifiable individuals is prohibited.
Content Standards: Entries must abide by the law of the land, suitable for all audiences, and must not contain defamatory, obscene, hateful, misleading, or partisan political-party content.
Minors: Participants under 18 years of age must submit their reel / video along with a verifiable consent of submission from his / her parent or legal guardian.
Shortlisting: Submission of an entry does not create any obligation on the Organisers to shortlist the entry. The Organisers reserve the right to verify eligibility and authenticity and to reject or disqualify any entry at their sole discretion.
Detailed Agreement: Shortlisted participants shall, as a condition of further participation, execute a detailed Participation Agreement covering, inter alia, confidentiality (NDA), intellectual property rights, licensing and usage rights, media permissions, representations and warranties, indemnities, code of conduct, eligibility requirements, and such other terms as the Organisers may prescribe. Failure to execute such agreement within the stipulated timeline may result in disqualification.
Final Decision: All decisions of the Organisers regarding eligibility, shortlisting, evaluation, and the competition shall be final and binding upon all the participating teams.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Timeline
Registrations
July 30th , 2026 (Thursday)– August 20th , 2026 (Thursday)
Shortlisting Phase
September 1st, 2026 (Tuesday) - September 10th, 2026 (Thursday)
Grand Finale
October 6th, 2026 (Tuesday) – October 8th, 2026 (Thursday)
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Award Ceremony:
October 9th, 2026 (Friday)
CCTNS Scheme had been conceptualized as a comprehensive and integrated system for enhancing the efficiency and effective policing at all levels and especially at the Police Station level in order to achieve the following key objectives:
Creating Centralized Databases
Creating State and Central levels databases on crime and criminals starting from FIRs.
Sharing Real-time Information
Enable easy sharing of real-time information/ intelligence across police stations, districts and States.
Prevention
Improved investigation and crime prevention.
Citizen Portals
Improved service delivery to the public/ stakeholders through Citizen Portals.
No. of participants
0+
Awards
0+
Tracks
0+
Cities
0+
Important Dates
Announcement
& Registration
& Registration
30th
July 2026
July 2026
Shortlisting Phase
1st - 10th
September 2026
September 2026
Grand Finale
6th - 8th
October 2026
October 2026
Award Ceremony
9th
October 2026
October 2026
Judging Criteria (AI Kavach):
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Innovation & Relevance to the Theme
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The solution must align well with the competition's theme and directly address the selected problem statement. The idea should demonstrate uniqueness, creativity, or innovation in its approach.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Feasibility of the Solution
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The solution should be practical and realistic to implement in real-world scenarios. It must be technically and economically viable, with resources or infrastructure being reasonably accessible. The potential challenges of implementing the solution should be identified, and appropriate strategies to address them must be outlined effectively.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Illustration of the Idea
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The idea must be clearly presented, highlighting key features, workflows, and functionality. Visual aids like diagrams, flowcharts, or lifecycle visualizations should be used effectively.
Grand Finale
Weightage:
Weightage:
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Technical Depth
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
Participants need to mention the coding and technologies or any special framework, libraries they have used. For any coding sample it is to be checked if the code sample is well-commented that anyone can understand what is the usability of that particular piece of code
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Presentation & Demonstration
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The PowerPoint presentation should be clear, organized, and comprehensive. It must effectively convey the idea using a balanced mix of visuals, text, and explanations to ensure the audience understands the solution.
Key Highlights:
Medallions and Certificates:
Awarded by the Territorial Army, these will serve as a prestigious recognition for participants' contributions and achievements during the event.
Attractive Prizes:
Top 10 teams to the TCQ Grand Finale National recognition, prizes and a live stage alongside the Bug Hunting and AI Kavach finalists.
Opportunity to Contribute to the Territorial Army:
Participants will have a chance to collaborate with the Territorial Army, working alongside defense professionals and contributing directly to the national defense landscape and interaction with Senior Government and Military Hierarchy
Honoring of Winners:
Awards to be presented by a Senior Official from the Government of India and opportunity to visit key defence establishment
Special Recognition Awards
Awards to be presented by Senior Official from the Government of India.
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Contribute to the future of the National Territorial Army’s defense technology! Register now to participate in Terrier Cyber Quest 2026 and turn your innovative ideas into reality.
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Explore the profiles of our winners from previous years.
Explore the profiles of our winners from previous years.
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