Summary of Generating Camera Failures As a Class Of Physics-based Adversarial Examples, by Manav Prabhakar and Jwalandhar Girnar and Arpan Kusari
Generating camera failures as a class of physics-based adversarial examples
by Manav Prabhakar, Jwalandhar Girnar, Arpan Kusari
First submitted to arxiv on: 23 May 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 This paper proposes a novel approach to generate physics-based adversarial samples, focusing on camera failures caused by physical processes rather than digital manipulations. The authors develop a stress-based simulation that models the breakdown of camera components, mimicking real-world failures. This simulated physical process is used to create images with realistic broken lens patterns. Additionally, the researchers design a neural emulator that learns the non-linear mapping between the physical mesh and stress propagation, enabling the generation of adversarial samples. The paper compares these generated samples with real and simulated adversarial examples using detection failure rates and Frechet Inception distance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates fake images to trick AI cameras like those used in self-driving cars or security systems. It does this by simulating how a camera might break due to physical stress, like a lens getting damaged. The scientists use computer simulations to model how the camera would look if it was broken, creating realistic pictures of “broken lenses”. They also teach a computer program (neural emulator) to understand how stress affects the camera’s image. By comparing these fake images with real ones that were created to trick cameras, the researchers can see how well their simulated images fool the AI systems. |
