Summary of Adversarial Robustness Of Bottleneck Injected Deep Neural Networks For Task-oriented Communication, by Alireza Furutanpey and Pantelis A. Frangoudis and Patrik Szabo and Schahram Dustdar
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication
by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, Schahram Dustdar
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Image and Video Processing (eess.IV)
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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 paper investigates the adversarial robustness of Deep Neural Networks (DNNs) in task-oriented communication systems using Information Bottleneck (IB) objectives. It empirically demonstrates that while IB-based approaches provide baseline resilience against attacks, they introduce new vulnerabilities through generative models for task-oriented communication. The study analyzes how bottleneck depth and task complexity influence adversarial robustness on several datasets. The findings show that Shallow Variational Bottleneck Injection (SVBI) provides less robustness than Deep Variational Information Bottleneck (DVIB) approaches, especially for complex tasks. Additionally, the paper reveals that IB-based objectives exhibit stronger robustness against attacks targeting salient pixels with high intensity compared to those perturbing many pixels with lower intensity. The study highlights security considerations for next-generation communication systems relying on neural networks for goal-oriented compression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Deep Neural Networks (DNNs) can protect themselves from being fooled by fake data. It uses a special way of teaching DNNs called Information Bottleneck (IB) to make them more resilient. But the researchers found that this approach has its own weaknesses, especially when used with generative models that help compress data for specific tasks. The study tested how different aspects of IB affect its robustness and found that using a shallower version of IB makes it less effective against attacks. The paper also shows that some types of attacks are more successful than others. Overall, the findings highlight important security concerns for future communication systems that rely on DNNs to compress data. |
