Secure Image Generation Using GANs and VAEs: Integrating Robust Security Measures against Adversarial Threats

Abstract

Evaluation of the generated images employs quantitative metrics such as Inception Score (IS) and Frechet Inception Distance (FID) to assess their quality and fidelity. IS measures the diversity and realism of images based on class distributions, while FID quantifies the similarity in feature distributions between generated and real images, providing insights into the effectiveness of the generative models. To fortify the integrity of generated content, the research advances security measures including adversarial input detection algorithms, authentication mechanisms such as digital signatures and watermarking, and encryption techniques like AES for secure data transmission and storage. These measures are integrated directly into the generative model architecture, validated through rigorous testing against adversarial attacks and real-world simulations to ensure robust performance. Research outcomes highlight significant advancements in detecting manipulated images and enhancing the reliability of generative models in diverse applications. The findings contribute to the evolving landscape of secure image generation technologies, offering insights and recommendations for future research directions aimed at advancing the intersection of generative models and cybersecurity.