Summary of Revealing Vulnerabilities Of Neural Networks in Parameter Learning and Defense Against Explanation-aware Backdoors, by Md Abdul Kadir et al.
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
by Md Abdul Kadir, GowthamKrishna Addluri, Daniel Sonntag
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a crucial challenge to Explainable Artificial Intelligence (XAI) strategies, as they can be manipulated by blinding attacks. These attacks alter neural networks’ predictions and explanations, providing misleading information while maintaining accuracy. To address this issue, the authors leverage statistical analysis to analyze changes in CNN weights after blinding attacks and introduce a method to limit attack effectiveness during evaluation. This defense mechanism achieves an approximate 99% reduction in Attack Success Rate (ASR) and 91% reduction in Mean Square Error (MSE) between original and defended explanations across three types of attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem with how artificial intelligence explains its decisions. Right now, bad guys can trick AI into giving false answers by adding tiny changes to the data it’s looking at. This is called a “blinding attack.” To stop this from happening, the authors used special math tricks to see what happens when an AI model gets attacked and then developed a way to make the model more secure. They tested their method against three different types of attacks and found that it was very effective in stopping the bad guys. This is important because it helps us trust artificial intelligence more. |
Keywords
* Artificial intelligence * Cnn * Mse
