Transferable Adversarial Attacks for Image and Video Object Detection

Understand

Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection.

• However, the existing attacking methods for object detection have two limitations: poor transferability, which denotes that the generated adversarial examples have low success rate to attack other kinds of detection methods, and high computation cost, which means that they need more time to generate an adversarial image, and therefore are difficult to deal with the video data.
• To address these issues, we utilize a generative mechanism to obtain the adversarial image and video.
• In this way, the processing time is reduced.