Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
<|reference_start|>Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors: Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks. Nonetheless, these techniques could potentially generate misleading explanations. Blinding attacks can drastically alter a machine learning algorithm's prediction and explanation, providing misleading information by adding visually unnoticeable artifacts into the input, while maintaining the model's accuracy. It poses a serious challenge in ensuring the reliability of XAI methods. To ensure the reliability of XAI methods poses a real challenge, we leverage statistical analysis to highlight the changes in CNN weights within a CNN following blinding attacks. We introduce a method specifically designed to limit the effectiveness of such attacks during the evaluation phase, avoiding the need for extra training. The method we suggest defences against most modern explanation-aware adversarial attacks, achieving an approximate decrease of ~99\% in the Attack Success Rate (ASR) and a ~91\% reduction in the Mean Square Error (MSE) between the original explanation and the defended (post-attack) explanation across three unique types of attacks.<|reference_end|>
@article{kadir2024revealing,
    title={Revealing Vulnerabilities of Neural Networks in Parameter Learning and
  Defense Against Explanation-Aware Backdoors},
    author={Md Abdul Kadir, GowthamKrishna Addluri, Daniel Sonntag},
    journal={arXiv preprint arXiv:2403.16569},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16569},
    primaryClass={cs.LG cs.CR cs.CV}
}