Recovering Chest X-RAY Images from Adversarial Attacks in Pneumonia Classification: Genetic Algorithm-based Adaptive Compression (GA-AC)

Abstract

Adversarial attacks have become a critical threat to the reliability of deep learning models applied to medical image processing. Techniques such as the Fast Gradient Sign Method (FGSM), the Iterative Fast Gradient Sign Method (I-FGSM), also known as the Basic Iterative Method (BIM), and the Projected Gradient Descent (PGD) can induce significant prediction errors, compromising diagnostic accuracy and patient safety. To ad- dress this challenge, this study proposes an innovative approach called Genetic Algorithm- based Adaptive Compression (GA-AC), developed to recover images perturbed by various types of adversarial attacks. The GA-AC method employs a genetic algorithm to opti- mize image compression parameters, aiming to maximize the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM), thereby preserving essen- tial diagnostic features in the restored images. Experiments conducted on multiple X-ray images demonstrated the effectiveness of GA-AC, which successfully restored model per- formance after different adversarial attacks, recovering, for example, the F1-score from 24.14% to 98.10% after the FGSM attack, and achieving similarly robust results against I-FGSM, and PGD.