detection and generation of face morphing attacks

presentation

1. Department of Information Technology Major Project Review-I Presentation On Generation and Detection of Face Morphing Attacks Team Mates Pavitra Vaishnavi -21J41A1242 P Madhav Reddy - 21J41A1254 Shaik Sameer - 21J41A1252 Nakka Rajeev - 21J41A1239 Under the Guidance of Mr.G.Joel Krupakar Assistant Professsor

2. Abstract Face morphing attacks, where synthetic images are created by combining two or more facial images to resemble multiple individuals, pose serious threats to security and privacy, enabling identity theft, passport fraud, and bypassing facial recognition systems. This paper analyzes these attacks, focusing on generation methods both traditional and deep learning based and their detection. We propose a novel detection framework combining traditional image processing and deep learning to identify artifacts like inconsistent texture patterns and facial landmarks. Extensive experiments on diverse datasets demonstrate the framework's high accuracy in detecting morphed faces with minimal false positives.

3. Objective This project aims to address face morphing attacks by enhancing generation and detection methods. It leverages deep learning for realistic morph creation and combines image processing with advanced models to detect subtle artifacts in morphed images. The goal is to improve the security and reliability of biometric systems against evolving threats.

4. Existing System This system for face morphing attacks address both generation and detection methods. Generation techniques, including traditional and deep learning-based approaches, create synthetic images by blending features of multiple individuals, posing serious security threats. Detection methods, using image processing and deep learning, identify artifacts like inconsistent texture patterns and facial landmarks in morphed images. However, current detection techniques face challenges in balancing high accuracy with low false positive rates, highlighting the need for more robust and reliable solutions to counter increasingly advanced morphing techniques.

5. Disadvantages Generation Methods : Traditional techniques rely on simple blending, resulting in artifacts and inconsistencies. Deep learning-based methods require significant training data and computational resources. Easy accessibility allows malicious actors to create convincing morphed images. Detection Methods : A lgorithms struggle to capture subtle morph artifacts, leading to false positives/negatives. Deep learning models may lack generalization and perform well only on specific datasets. Ongoing advancements in morph generation outpace detection techniques, creating persistent challenges.

6. Proposed System The proposed system addresses the limitations of existing methods in generating and detecting face morphing attacks. It leverages deep learning to create realistic morphs with minimal artifacts using advanced neural architectures and diverse datasets. For detection, a novel framework combines traditional image processing and state-of-the-art deep learning to identify subtle artifacts like texture inconsistencies and distorted facial features. By analyzing facial landmarks and biometric characteristics, the system enhances detection accuracy. It focuses on robustness and generalization to perform reliably across diverse datasets and adapt to evolving morphing techniques while maintaining high accuracy and low false positive rates.

7. Advantages Generation Methods : Utilizes deep learning to create realistic morphs with reduced artifacts and high fidelity, enhancing quality and making detection more challenging. Detection Methods : Combines image processing and deep learning to identify subtle morph artifacts like texture inconsistencies and distorted facial features for improved accuracy. Robustness and Generalization : Ensures reliable performance across diverse datasets and evolving morphing techniques while minimizing false positives/negatives. Enhanced Security : Strengthens facial recognition and biometric authentication systems by reducing risks like identity theft and ensuring reliable detection of morphing attacks.

8. SYSTEM REQUIREMENTS :- HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Ram : 512 Mb. SOFTWARE REQUIREMENTS :- Operating system : Windows. Coding Language : python. Designing : HTML, CSS, JAVASCRIPT .

9. Thank You