Summary of Impact Of Architectural Modifications on Deep Learning Adversarial Robustness, by Firuz Juraev et al.
Impact of Architectural Modifications on Deep Learning Adversarial Robustness
by Firuz Juraev, Mohammed Abuhamad, Simon S. Woo, George K Thiruvathukal, Tamer Abuhmed
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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 an experimental evaluation of the impact of model modifications on the robustness of deep learning models against adversarial attacks. The authors examine the effects of various model variations on their resistance to different types of attacks, aiming to provide insights into maintaining the reliability and safety of deep learning models in safety-critical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on the challenges posed by rapid advancements in deep learning for safety-critical applications like self-driving vehicles, drones, and robots. It highlights the need for thorough analysis to determine the impact of model modifications on their robustness against adversarial attacks. The authors’ methodology involves evaluating the robustness of different models against various types of attacks. |
Keywords
» Artificial intelligence » Deep learning
