Robustness and Visual Explanation for Black Box Image, Video, and ECG
  Signal Classification with Reinforcement Learning
Soumyendu Sarkar Ashwin Ramesh Babu Sajad Mousavi Vineet Gundecha Avisek Naug Sahand Ghorbanpour
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.