July 23rd - August 30th
Deadline
LOCATION:
New Delhi
New Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
REGISTRATION OPEN
Apply by August 30
Indian Army
Terrier Cyber Quest 2025
75 Years of Territorial Army
Building on the tremendous success of Terrier Cyber Quest 1.0, the Indian Army Terrier Cyber Quest 2.0 aims to delve deeper into fostering innovation and collaboration in critical domains like securing emerging technologies like drone technology, AI/ML, Quantum etc. 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.
July 23rd - August 30th
10:00 - 19:00
10:00 - 19:00
Deadline
EVENT LOCATION
LOCATION: Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
Indian Army Terrier Cyber Quest 2025 will have two tracks:
Track 1
Bug Hunting
A race against the clock in a high-stakes cybersecurity hackathon—starting with a CTF and culminating in a 36-hour live bug hunt in a customized environment. Finalists will dig deep to uncover vulnerabilities in a simulated Indian Army environment.
Registration
Registration for Terrier Cyber Quest 2025 begins on July 23rd, 2025. Participants can register individually or in teams of up to three members. To strengthen their application, participants are encouraged to share links to certifications or badges earned in the cybersecurity domain.
Shortlisting
A 8-10 hour virtual elimination round will be conducted in this track. Teams/Participants will be provided with a link to the CTF Portal, where they will be competing against each other.
● It will be a short Capture the Flag (CTF) event.
● Participants will be provided with puzzles/programs/system/files containing security vulnerabilities.
● Each puzzle has a secret key called a 'flag' embedded within it. Finding the flag proves that participants have solved the particular challenge, and submitting the flag earns points.
● Each problem statementhas its own points, which depend on the difficulty of the problem.
● The scoring proceduredepends on how many points participants have earned and how much time they havetaken to submit the flags.
● Participants also may beasked to submit a Proof-of-Concept document after this phase.
● Top 10 teams/participantson the leadership board will be selected as the finalists for this track.
● It will be a short Capture the Flag (CTF) event.
● Participants will be provided with puzzles/programs/system/files containing security vulnerabilities.
● Each puzzle has a secret key called a 'flag' embedded within it. Finding the flag proves that participants have solved the particular challenge, and submitting the flag earns points.
● Each problem statementhas its own points, which depend on the difficulty of the problem.
● The scoring proceduredepends on how many points participants have earned and how much time they havetaken to submit the flags.
● Participants also may beasked to submit a Proof-of-Concept document after this phase.
● Top 10 teams/participantson the leadership board will be selected as the finalists for this track.
Grand Finale
The Grand Finale will be a 36-hour in-person challenge, where the top 10 shortlisted participants/teams will be invited to compete. In this intensive round, participants will engage in live bug hunting within a secure, controlled environment designed to simulate real-world conditions. The target environment will reflect a customized system stack used in national infrastructure. Participants will be required to identify and document vulnerabilities. The setup may include elements inspired by specific organizational use cases and configurations, with dummy data mimicking operational scenarios—ensuring a safe yet realistic experience. Points will be awarded based on the severity, originality, and complexity of the bugs found. A panel of experts will verify the discovered bugs and determine the final scores after the Grand finale round gets over. The top 3 participants/teams will be declared winners based on their cumulative performance.
Timeline
Announcement
July 23rd, 2025
Registrations
July 23rd - August 30th, 2025
Shortlisting of participants
September 1st - 12th, 2025
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 .
Grand Finale
September 17th - 18th, 2025
Award Ceremony
October 7th, 2025
Track 2
Datathon
The Datathon is a data-centric innovative challenge designed to test participants' ability to build robust technological solutions/models that can solve real-world problems related to defense and national security. This year’s focus is on predictive threat intelligence and anomaly detection using large-scale datasets
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:
Registration:
Registration for Terrier Cyber Quest 2025 begins on July 23rd, 2025. Participants can register individually or in teams of up to three members and submit an abstract explaining their proposed approach to the challenge in PPT/PDF format. They should outline the problem they aim to solve, the methodology and technological stack they intend to use; and any relevant experience or past work in the specific domain.
Shortlisting
Participants/Teams will be evaluated based on the document/brief, the quality of their writeup/idea and PowerPoint presentation submitted during registrations on the basis of set judging criteria. Top 10 (maximum 10x3=30 head counts) Teams/Participants will be shortlisted for the Final Round
Grand Finale:
During the Grand Finale, shortlisted participants will have 36 hours to transform their proposed solutions into working prototypes. They will have access to mentors and necessary resources to assist in the development process. Teams are encouraged to focus on functionality, usability, and scalability within the time limit. They may fine-tune their models or develop new ones in real time, presenting live solutions that are evaluated on performance, speed, precision, and scalability, as per mentors’ suggestions
● 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.
Problem Statements:
1. Theme: Drone Flight Anomaly Detection
a. Objective: Build a predictive model to detect anomalies in drone flight paths using telemetry data.
b. Dataset: Time-series logs of altitude, velocity, yaw, pitch, battery, and GPS drift.
c. Challenge: Identify malfunction patterns, signal jamming, or unauthorized diversions.
d. Impact: Prevent mid-air mission failures or hijack scenarios during surveillance ops.
a. Objective: Build a predictive model to detect anomalies in drone flight paths using telemetry data.
b. Dataset: Time-series logs of altitude, velocity, yaw, pitch, battery, and GPS drift.
c. Challenge: Identify malfunction patterns, signal jamming, or unauthorized diversions.
d. Impact: Prevent mid-air mission failures or hijack scenarios during surveillance ops.
2. Theme: Quantum Machine Learning for Threat Detection
○ Quantum Malware Hunter
Problem: Use quantum-enhanced ML to detect unknown malware patterns in real-time.
Dataset: Simulated network traffic or malware signatures.
○ AI + Quantum = Early Ransomware Detection
Problem: Build a hybrid AI system (classical + quantum) that predicts ransomware deployment patterns from user behavior logs.
○ Quantum Malware Hunter
Problem: Use quantum-enhanced ML to detect unknown malware patterns in real-time.
Dataset: Simulated network traffic or malware signatures.
○ AI + Quantum = Early Ransomware Detection
Problem: Build a hybrid AI system (classical + quantum) that predicts ransomware deployment patterns from user behavior logs.
Timeline
Announcement
July 23rd, 2025
Registration
July 23rd - August 30th, 2025
Shortlisting of participants
September 1st - 12th, 2025
Grand Finale
September 19th -20th, 2025
Award Ceremony
October 7th, 2025
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 .
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
23
July
30
August
17-20
September
Shortlisting
period
period
07
October
Registration
Starts
Starts
Registration
Closes
Closes
Final
Round
Round
Award Ceremony
Judging Criteria (Track 2):
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:
Winners will receive exciting prizes, enhancing the competitive spirit and rewarding innovative solutions that can contribute to national security
Boarding and Lodging:
All participants will be provided with comfortable boarding and lodging facilities for the duration of the event, ensuring a seamless experience.
Opportunity to Contribute to the Territorial Army:
Participants will have a rare chance to collaborate with the Territorial Army, working alongside defense professionals and contributing directly to the national defense landscape
Honoring of Winners:
Awards to be presented by an Honourable Member of Parliament or a Senior Official from the Government of India.
Special Recognition Awards
Awards to be presented by an Honourable member of Parliament or a Senior Official from the Government of India.
Join Us
Contribute to the future of the National Territorial Army’s defense technology! Register now to participate in Terrier Cyber Quest 2025 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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