Summary of A Minimax Approach Against Multi-armed Adversarial Attacks Detection, by Federica Granese et al.
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
by Federica Granese, Marco Romanelli, Siddharth Garg, Pablo Piantanida
First submitted to arxiv on: 4 Feb 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 proposed framework aggregates the soft-probability outputs of multiple pre-trained detectors according to a minimax approach, providing a mathematically sound and modular solution for improving the resilience of state-of-the-art adversarial examples detectors. By leveraging multi-armed adversarial attacks, which simultaneously use multiple algorithms and objective loss functions at evaluation time, this framework outperforms individual detectors on popular datasets such as CIFAR10 and SVHN. The results demonstrate the effectiveness of this approach in detecting and mitigating multi-armed adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make AI models better at defending against fake data by combining the strengths of many different algorithms. Imagine having a team of detectives, each with their own special skills, working together to solve a mystery. The new framework does something similar, using multiple detectors to identify and block fake data. This helps keep AI systems safe from attacks that try to trick them into making mistakes. |
Keywords
* Artificial intelligence * Probability
