Image integrity analysis under defense approaches against adversarial patches

Adversarial patches pose a significant threat to image recognition systems, exploiting small, maliciously crafted patterns to mislead models into incorrect classifications. Unlike traditional adversarial examples, adversarial patches operate under real-world constraints, making defense against them a challenging task. This study introduces an alternative defense strategy that employs partial masking to mitigate the impact of adversarial patches without relying on precise patch detection. Various global masking patterns were evaluated to determine the best balance between adversarial robustness and image integrity. The proposed method effectively reduces attack success rates while maintaining reasonable classification accuracy, demonstrating its potential to ensure protection in real-world applications without dependency on the detection methods.