Transferable Adversarial Attacks for Image and Video Object Detection

TL;DR

This paper introduces a GAN-based method to generate transferable adversarial examples for image and video object detection, reducing computation time and improving attack success across different detection models.

Contribution

The paper proposes a novel GAN framework that enhances transferability and efficiency of adversarial attacks on object detection models for images and videos.

Findings

Improved transferability of adversarial examples across detection models.

Reduced generation time for adversarial images and videos.

Effective attacks on both proposal-based and regression-based detectors.

Abstract

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. To enhance the transferability, we destroy the feature maps extracted from the feature network, which usually constitutes the basis of object detectors. The proposed method is based on the Generative Adversarial Network (GAN) framework, where we combine the high-level class loss and low-level feature loss to jointly train the adversarial example generator. A series of experiments conducted on PASCAL VOC and ImageNet VID datasets show that our method can efficiently generate image and video adversarial examples, and more importantly, these adversarial examples have better transferability, and thus, are able to simultaneously attack two kinds of representative object detection models: proposal based models like Faster-RCNN, and regression based models like SSD.