Summary of Fooling Neural Networks For Motion Forecasting Via Adversarial Attacks, by Edgar Medina et al.
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks
by Edgar Medina, Leyong Loh
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 A novel approach is proposed to address the open problem of human motion prediction in autonomous driving and safety applications. The study focuses on adversarial attacks against multi-regression models, including GCNs and MLP-based architectures, which have not been extensively explored in this domain. Experimental results demonstrate that these models are susceptible to low-level perturbations, highlighting the need for robustness and security measures. Additionally, the work shows that simple 3D transformations, such as rotations and translations, can significantly impact model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers have developed new ways to make computers predict human movements more accurately. They found that these predictions can be easily fooled by small changes in the data, which is concerning for applications like self-driving cars. The team also discovered that simple movements, like rotating or moving an arm, can greatly affect how well the computer predicts human motion. |
Keywords
» Artificial intelligence » Regression
