Summary of Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3d Objects, by Abdurrahman Zeybey et al.
Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects
by Abdurrahman Zeybey, Mehmet Ergezer, Tommy Nguyen
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 the Masked Iterative Fast Gradient Sign Method (M-IFGSM), a novel approach for generating adversarial noise targeting the CLIP vision-language model. The method focuses perturbations on masked regions, degrading the performance of CLIP’s zero-shot object detection capability when applied to 3D models. Using the CO3D dataset, the authors demonstrate that M-IFGSM effectively reduces the accuracy and confidence of the model, making it nearly imperceptible to human observers. The paper highlights the risks of adversarial attacks on 3D models in applications such as autonomous driving, robotics, and surveillance. The significance of this research lies in its potential to expose vulnerabilities in modern 3D vision models, including radiance fields, prompting the development of more robust defenses and security measures in critical real-world applications. The authors demonstrate a significant drop in top-1 accuracy from 95.4% to 12.5% for train images and from 91.2% to 35.4% for test images, with confidence levels reflecting this shift from true classification to misclassification. The paper’s contributions lie in its ability to generate effective adversarial noise targeting the CLIP model, making it a valuable addition to the field of computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make machines that can see and understand 3D images more vulnerable to attacks by tricking them into seeing things that aren’t really there. This is important because these machines are used in things like self-driving cars and robots, where they need to be able to accurately see what’s around them. The researchers created a special method called M-IFGSM that can make the machines think things are in one place when they’re actually somewhere else. They tested this on some 3D images and found that it worked really well. This means that if someone wanted to trick these machines, they could use this method to make them see things that aren’t really there. This is important because if these machines can be tricked into seeing false information, it could have bad consequences in real-world applications like self-driving cars or surveillance systems. |
Keywords
» Artificial intelligence » Classification » Language model » Object detection » Prompting » Zero shot
