Summary of Targeted View-invariant Adversarial Perturbations For 3d Object Recognition, by Christian Green and Mehmet Ergezer and Abdurrahman Zeybey
Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition
by Christian Green, Mehmet Ergezer, Abdurrahman Zeybey
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); 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 introduces View-Invariant Adversarial Perturbations (VIAP), a novel method to craft robust adversarial examples that remain effective across multiple viewpoints in 3D object recognition. The proposed approach enables targeted attacks, manipulating recognition systems to classify objects as specific labels using a single universal perturbation. Leveraging a dataset of diverse rendered 3D objects, the paper demonstrates the effectiveness of VIAP in both targeted and untargeted settings. The findings highlight VIAP’s potential for real-world applications, such as testing the robustness of 3D recognition systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adversarial attacks are a big problem in recognizing 3D objects from different angles. This new method, called View-Invariant Adversarial Perturbations (VIAP), helps create fake examples that work across many views. Instead of just changing one angle, VIAP can make an object look like something else from any angle. The researchers tested this method on a big dataset and showed it works really well. This could be important for testing how well 3D recognition systems are designed. |
