! The results of this competition will not be benchmarked automatically. Each submission will be reviewed manually by a jury of experts.
Abstract:
This challenge addresses the critical vulnerability of deepfake attacks in autonomous systems and vehicles relying on biometric authentication methods like fingerprint or facial recognition. Participants will develop innovative solutions leveraging cutting-edge quantum-resistant security protocols, zero-knowledge proofs, and Root of Trust to create a robust defense against sophisticated deepfake attempts.
The focus is on creating a multi-layered security framework that combines these advanced technologies to ensure the integrity and authenticity of biometric data in autonomous systems. The ultimate goal is to develop a software solution integrated with or without the SRAM PUF Castle Shield token. This comprehensive approach will not only detect and mitigate deepfake threats in real-time but also provide long-term security against potential quantum computing attacks. By incorporating quantum-resistant encryption algorithms for secure data transmission, the solution will ensure continued reliability and trustworthiness of autonomous vehicle perception modules and biometric authentication systems. This challenge aims to push the boundaries of cybersecurity in autonomous technologies, safeguarding against both current and future threats in an increasingly complex digital landscape.
Business Problem:
The autonomous vehicles and smart transportation industry is facing a critical security challenge that threatens its growth, user trust, and long-term viability. This problem has two main components:
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Vulnerability to Deepfake Attacks:
Current biometric authentication systems used in the autonomous vehicles and smart transportation infrastructure are susceptible to sophisticated deepfake attacks. These attacks can potentially bypass facial recognition, voice authentication, and even sensor data verification systems, compromising the vehicle security, user safety, and operational integrity.
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Quantum Computing Threat:
The impending arrival of practical quantum computing poses a significant risk to existing encryption methods. This threatens to undermine the entire security infrastructure of autonomous systems, potentially exposing sensitive user data and critical vehicle operations to malicious actors.
Additionally, the conventional practice of storing biometric data for authentication processes creates a substantial privacy risk. Unlike passwords, biometric data is irreplaceable if compromised, making it an attractive target for cybercriminals and raising serious concerns among users and regulators. The threat extends beyond just autonomous vehicles to any system relying on remote identity verification, making it a broader cybersecurity challenge for various industries.
For example, a deepfake video may trick a vehicle's driver monitoring system into believing that an unauthorized user is the registered owner, allowing them to take control of the vehicle.
The presence of these security vulnerabilities could result in:
- A decrease in consumer confidence in automated vehicle technology.
- An increase in liability risks for manufacturers and operators.
- Compromised safety for passengers and other road users.
- Damage to a company's reputation and many other consequences.
Inspiration:
To address this critical business problem, we need to inspire bold transformative solutions that redefine security in autonomous systems. Imagine a future where autonomous vehicles stand as unbreachable digital fortresses, safeguarding user privacy and ensuring the seamless, fearless operation of self-driving technology.
We challenge innovators to develop a security framework that merges quantum-resistant encryption, zero-knowledge biometric verification, and real-time deepfake detection. This solution should protect autonomous systems from today’s threats while future-proofing them against tomorrow’s challenges. It should authenticate users without storing sensitive data, continuously validate sensor inputs, and adapt dynamically to new security risks. We envision a security architecture that not only defends but also empowers the full potential of autonomous technology, ensuring safety, privacy, and trust in an increasingly complex digital landscape.
Technical Problem Statement:
Autonomous vehicles and systems increasingly use biometric authentication like facial recognition and fingerprint scanning for security. However, they are vulnerable to deepfake attacks that manipulate biometric data, creating serious security risks. Furthermore, the rise of quantum computing threatens to compromise existing cryptographic methods, exposing biometric systems to future attacks.
The challenge is to develop an innovative, multi-layered security framework that can detect and defend against real-time deepfake attacks on biometric systems while being resistant to future quantum computing threats. The key requirements for this solution include:
- Quantum-resistant encryption algorithms to secure data transmission and storage.
- Zero-knowledge proofs to authenticate users without exposing sensitive biometric data.
- A robust Root of Trust (RoT) mechanism, potentially leveraging hardware-based technologies like SRAM PUF (Castle Shield Token), to ensure the integrity of the system https://github.com/Castle-Shield/QuantumDeepfake.git.
- Real-time deepfake detection algorithms capable of identifying and mitigating manipulation of biometric data inputs.
The solution should be designed for seamless integration with existing autonomous systems and biometric authentication modules, such as those in autonomous vehicles, without compromising performance or scalability.
Current Classical Approaches:
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Machine Learning Detection:
ML models, particularly CNNs and vision transformers, are widely used to detect deepfakes by spotting inconsistencies in facial movements, lighting, and texture. These models improve by training on datasets like the DeepFake Detection Challenge (DFDC). However, this remains an ongoing challenge as deepfake generation evolves.
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Liveness Detection:
This method checks for signs of live human presence, such as blinking and blood flow patterns, and is used in facial recognition systems to prevent deepfakes from deceiving biometric authentication.
Future Approaches:
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Post-Quantum Cryptography (PQC):
With the rise of quantum computing, future cryptographic defenses will require quantum-resistant algorithms like CRYSTALS-Kyber. These will secure digital authentication systems, helping counter deepfakes even if current cryptographic methods fail.
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Cryptographic Provenance:
Embedding cryptographic signatures into media when created can authenticate its originality. The Coalition for Content Provenance and Authenticity (C2PA) is exploring this.
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Hardware Integration:
Cameras with public key infrastructure (PKI) could sign captured media, ensuring authenticity and preventing tampering.
Technology:
Developers are encouraged to leverage any advanced technologies necessary to create the application and biometric authentication system for autonomous vehicles. The goal is to explore innovative solutions that enhance performance, security, and user experience.
Reference Repository
Quantum Hardware:
While the primary focus is on developing quantum-resistant cryptographic solutions, we encourage participants to explore the integration of emerging quantum technologies to further enhance security. Consider using quantum simulators to test the robustness of proposed post-quantum cryptographic schemes.
Participants should investigate how these quantum hardware elements can be combined with post-quantum cryptography (PQC) algorithms to create a hybrid solution that is resistant to both classical and quantum computing attacks. This could potentially be more secure than purely classical PQC implementations and able to leverage the unique properties of quantum systems for enhanced randomness and security.
The goal is to develop a forward-looking security framework that not only defends against future quantum threats but also begins to harness quantum technologies for improved cryptographic strength, providing a bridge between current classical systems and future quantum-enabled security infrastructures.
Desired Outcomes:
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Biometric Authentication:
Implement biometric authentication for autonomous vehicles with robust mechanisms to detect and prevent deepfake attacks.
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Quantum-Resistant Encryption:
Utilize Post-Quantum Cryptography (PQC) to protect the software against quantum computing threats, ensuring low latency and scalability.
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Zero-Knowledge Proof (ZKP):
Enable privacy-preserving authentication, ensuring a False Acceptance Rate (FAR) below 0.01%, protecting sensitive biometric data.
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Root of Trust:
Leverage the Castle Shield Token as a hardware-based Root of Trust to provide system integrity with minimal latency.
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Real-Time Deepfake Detection:
Achieve 99% accuracy in real-time deepfake detection, immediately blocking access when a deepfake is identified, with millisecond-level processing latency.
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User-Friendly Interface:
Develop a user interface (UI) with high usability, providing a seamless and intuitive experience for users.
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Maintain Low Error Rates:
Ensure both security and user convenience by maintaining a False Acceptance Rate (FAR) below 0.01% and a False Rejection Rate (FRR) below 1%.
Evaluation Criteria & Numerical Metrics:
Submissions for the biometric authentication and security framework for autonomous vehicles will be evaluated based on the following criteria:
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Architecture Design (10%):
- Criteria: Innovation, modularity, and clarity in the proposed security framework architecture, focusing on how well it integrates post-quantum cryptography (PQC), deepfake detection, and biometric authentication alongside the use of the Castle Shield Token as a Root of Trust (RoT).
- Metric: Design evaluation score.
- Target: ≥ 90% (based on technical review).
- Description: The architecture should demonstrate innovative methods to ensure security, low latency, and scalability while protecting against quantum and deepfake threats. A detailed document with steps to set up the software on other systems needs to be included.
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Implementation (10%):
- Criteria: Practicality, functionality, and scalability of the solution’s implementation, with emphasis on real-time biometric authentication, deepfake detection, and quantum-resistant encryption.
- Metric: Functional evaluation score.
- Target: ≥ 85% (based on system functionality and integration testing).
- Description: The solution should be scalable, efficiently integrated into autonomous systems, and effective in real-world use cases. User satisfaction with the interface, ease of use, and overall experience should also be measured.
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Performance (40%):
- Criteria: Performance of the biometric authentication system, including deepfake detection and cryptographic processes, evaluated through accuracy, speed, and system responsiveness. Specific metrics include:
- Authentication Accuracy: ≥ 99% for biometric inputs.
- Deepfake Detection Accuracy: ≥ 99% with ≤ 10ms latency.
- False Acceptance Rate (FAR): ≤ 0.01%.
- False Rejection Rate (FRR): ≤ 1%.
- Metric: Performance evaluation score.
- Target: ≥ 90% (based on performance tests).
- Description: The system should demonstrate high accuracy and low latency in biometric authentication and deepfake detection, ensuring secure and real-time operation.
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Privacy Consideration and Innovation Factor (30%):
- Criteria: Innovative approaches for robustness in preserving user privacy, particularly in the use of Zero-Knowledge Proofs (ZKP) and Root of Trust (RoT) for authentication.
- Metric: Privacy evaluation score.
- Target: ≥ 85% (based on privacy and data handling review).
- Description: The system should ensure that sensitive biometric data is fully protected using ZKP to maintain privacy throughout the authentication process. Additionally, it should explore innovative ways to enhance security using RoT.
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Use of Quantum-Resistant Technologies (10%):
- Criteria: Effective implementation of post-quantum cryptography (PQC) to secure biometric and system data against quantum computing threats, including data encryption and transmission security.
- Metric: Quantum technology evaluation score.
- Target: ≥ 80% (based on the use of PQC and resistance to quantum attacks).
- Description: The solution should demonstrate strong integration of quantum-resistant encryption algorithms, ensuring long-term security against future quantum threats.
These criteria will provide a comprehensive evaluation of the solution’s technical design, implementation, performance, privacy safeguards, and future-proofing against quantum computing risks.
Important Information:
Participants are allowed to incorporate open-source code in their solutions. However, direct replication of existing solutions is not permitted. Each submission must clearly demonstrate originality by highlighting new developments, improvements, or innovations applied to the open-source components.
Participants must include a section in their documentation that outlines:
- Innovative Enhancements: What new features, algorithms, or approaches were introduced to improve or modify the open-source code.
- Wow Factor: What makes the solution stand out, focusing on any unique or breakthrough aspects that showcase innovation and creativity.
Failure to demonstrate significant improvements or originality in the solution will result in disqualification.
References:
- How deepfake tech is speeding up autonomous vehicle development - TechHQ https://techhq.com/2020/07/how-deepfake-tech-is-speeding-up-autonomous-vehicle-development/
- A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System https://arxiv.org/pdf/2409.17311
- Inside Quantum Technology’s Inside Scoop: Quantum and Deepfake Technology - Inside Quantum Technology https://www.insidequantumtechnology.com/news-archive/inside-quantum-technologys-inside-scoop-quantum-and-deepfake-technology-2/
- Face Liveness Detection Based on Skin Blood Flow Analysis (mdpi.com) https://www.mdpi.com/2073-8994/9/12/305
- Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images https://cdn.techscience.cn/files/CMES/2024/TSP_CMES-139-1/TSP_CMES_30640/TSP_CMES_30640.pdf
- Deepfakes Detection Techniques Using Deep Learning: A Survey (scirp.org) https://www.scirp.org/journal/paperinformation?paperid=109149
- The Post-Quantum Cryptography Algorithms are finalized! Now what? (thalesgroup.com) https://cpl.thalesgroup.com/blog/encryption/post-quantum-cryptography-algorithms
- Post Quantum Cryptography: Techniques Challenges Standardization and Directions for Future Research https://ar5iv.labs.arxiv.org/html/2202.02826
- Solutions to Deepfakes: Can Camera Hardware Cryptography and Deep Learning Verify Real Images? https://ar5iv.labs.arxiv.org/html/2407.04169v1
- Post-quantum Cryptography - Microsoft Research https://www.microsoft.com/en-us/research/project/post-quantum-cryptography/