Experimental Exploration of the Power of Conditional GAN in Image Reconstruction-Based Adversarial Attack Defense Strategies

Adversarial attacks pose a significant threat to the reliability and security of deep learning models, particularly in image processing applications. Defending against these sophisticated manipulations requires innovative strategies, with Generative Adversarial Networks (GANs) emerging as a promising solution. This paper presents an experimental exploration of the power of conditional Generative Adversarial Networks (cGANs) in image reconstruction-based strategies for defending against adversarial attacks. Our study involves a comparative analysis of four distinct image reconstruction models: the traditional GAN-based Defense-GAN, the cGAN-based method exemplified by pix2pix, a hybrid approach combining pix2pix with perceptual loss, and a generator model centered around residual blocks. The results of our experiments demonstrate that cGAN models exhibit significantly enhanced efficacy in defending against adversarial attacks compared to other image reconstruction methods. This superiority is attributed to the inherent characteristics of cGANs, which we delve into in detail. The findings provide crucial insights for developing more robust defense strategies against adversarial attacks in diverse image processing and machine learning applications.