eXIAA: eXplainable Injections for Adversarial Attack

The research presented in "eXIAA: eXplainable Injections for Adversarial Attack" highlights a significant vulnerability in explainable AI methods, particularly in the image domain. This aligns with findings from related studies like "CertMask," which addresses adversarial patch attacks that can mislead deep vision models, and "MOBA," which discusses vulnerabilities in LiDAR-based 3D object detection systems. Both articles emphasize the pressing need for robust defenses against adversarial attacks, underscoring the critical implications for safety in AI applications. The low requirements of the eXIAA method expose a broader issue within the field, raising alarms about the reliability of existing explainability techniques.