Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909177230655488 |
|---|---|
| author | Sarkar, Soumyendu Babu, Ashwin Ramesh Mousavi, Sajad Gundecha, Vineet Naug, Avisek Ghorbanpour, Sahand |
| author_facet | Sarkar, Soumyendu Babu, Ashwin Ramesh Mousavi, Sajad Gundecha, Vineet Naug, Avisek Ghorbanpour, Sahand |
| contents | We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion types. The novel RL method outperforms state-of-the-art methods for all three applications, proving its efficiency. Our RL approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For applications such as ECG analysis, our platform highlights critical ECG segments for clinicians while ensuring resilience against prevalent distortions. This comprehensive tool aims to bolster both resilience with adversarial training and transparency across varied applications and data types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning Sarkar, Soumyendu Babu, Ashwin Ramesh Mousavi, Sajad Gundecha, Vineet Naug, Avisek Ghorbanpour, Sahand Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Multiagent Systems We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion types. The novel RL method outperforms state-of-the-art methods for all three applications, proving its efficiency. Our RL approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For applications such as ECG analysis, our platform highlights critical ECG segments for clinicians while ensuring resilience against prevalent distortions. This comprehensive tool aims to bolster both resilience with adversarial training and transparency across varied applications and data types. |
| title | Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Multiagent Systems |
| url | https://arxiv.org/abs/2403.18985 |