Multimodal Attack Detection for Action Recognition Models

arXiv:2404.10790v1 Announce Type: cross
Abstract: Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using these models in practice raises significant security concerns. However, there are very few works which focus on defending against or detecting attacks. In this work, we propose a novel universal detection method which is compatible with any …

abstract action recognition adversarial adversarial machine learning arxiv attacks concerns cs.ai cs.cr cs.cv cs.lg detection however machine machine learning multimodal practice raises recognition research security security concerns show type video