Keep It Real: Challenges in Attacking Compression-Based Adversarial Purification

The article "Keep It Real: Challenges in Attacking Compression-Based Adversarial Purification" addresses the difficulties encountered when using compression-based techniques to defend against adversarial attacks on images (F1). It highlights that strong white-box and adaptive attacks remain effective against these defenses, posing significant challenges (F2). A key factor complicating the attack process is the high realism achieved in the reconstructed images after purification, which makes it harder to identify and exploit vulnerabilities (F3). These findings suggest that current evaluation methods may not fully capture the complexities involved in such defenses. Consequently, the article emphasizes the necessity for more comprehensive evaluations to better understand and improve compression-based adversarial purification strategies (F4). This underscores the ongoing need for robust assessment frameworks in the field of adversarial machine learning.