Summary of Robustness and Visual Explanation For Black Box Image, Video, and Ecg Signal Classification with Reinforcement Learning, by Soumyendu Sarkar et al.
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
by Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Avisek Naug, Sahand Ghorbanpour
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The presented generic Reinforcement Learning (RL) framework optimizes the crafting of adversarial attacks on various model types, including ECG signal analysis, image classification, and video classification. The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and different distortion types. This novel RL method outperforms state-of-the-art methods for all three applications, demonstrating its efficiency. The approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For ECG analysis, the platform highlights critical segments for clinicians while ensuring resilience against prevalent distortions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a Reinforcement Learning framework to create adversarial attacks on different types of machine learning models. It’s designed to work with one-dimensional (ECG), two-dimensional (images), and three-dimensional (videos) data. The goal is to identify important parts of the data and make mistakes in classification with only small changes. The new method does better than existing methods for all three types of data, showing it’s effective. It also creates more detailed maps that help understand how image classification models work. |
Keywords
» Artificial intelligence » Classification » Image classification » Machine learning » Reinforcement learning
