Quantum-Enhanced Photo Morphing Avoidance System for Women Safety

Abstract

The rapid advancement of artificial intelligence and digital image editing technologies has increased the prevalence of face morphing attacks, where multiple facial images are blended to create realistic synthetic photos. These images can bypass biometric authentication systems and are often misused in cyber harassment, identity theft, and blackmail, particularly targeting women. Recent deep learning techniques, such as hybrid LSTM-CNN models, morph detection frameworks, and similarity-based approaches, have improved the accuracy of identifying such attacks. Additionally, Vision Transformer models and explainable AI (XAI) techniques enhance detection reliability and provide interpretable results.However, most existing systems focus only on detection and lack mechanisms for ensuring long-term data integrity and security. With the rise of quantum computing, traditional cryptographic methods are becoming vulnerable, highlighting the need for quantum-resistant security solutions. Techniques like post-quantum digital signatures, secure biometric authentication, and image fingerprinting improve data protection and verification.To address these challenges, this paper proposes a Quantum-Enhanced Photo Morphing Avoidance System for Women Safety. The system combines AI-based morph detection with quantum-resistant cryptographic fingerprinting to ensure both accurate detection and secure, tamper-proof verification. This approach provides a reliable and future-ready solution for preventing image-based cyber crimes and enhancing women’s safety.