Report
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
| العنوان: | Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters |
|---|---|
| المؤلفون: | Sarkar, Soumyendu, Babu, Ashwin Ramesh, Mousavi, Sajad, Gundecha, Vineet, Ghorbanpour, Sahand, Naug, Avisek, Gutierrez, Ricardo Luna, Guillen, Antonio |
| سنة النشر: | 2025 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create adversarial examples. The platform uses a Reinforcement Learning agent to add minimum distortion to input images while still causing misclassification by the target model. The agent uses a novel dual-action method to explore the input image at each step to identify sensitive regions for adding distortions while removing noises that have less impact on the target model. This dual action leads to faster and more efficient convergence of the attack. The platform can also be used to measure the robustness of image classification models against specific distortion types. Also, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets. The proposed platform outperforms state-of-the-art methods in terms of the average number of queries required to cause misclassification. This advances trustworthiness with a positive social impact. Comment: Under Review for 2025 AAAI Conference on Artificial Intelligence Proceedings |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2501.14122 |
| رقم الانضمام: | edsarx.2501.14122 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |