Dual Attribute Adversarial Camouflage toward camouflaged object detection

Abstract

The object detectors can precisely detect the camouflaged object beyond human perception. The investigations reveal that the CNNs-based (Convolution Neural Networks) detectors are vulnerable to adversarial attacks. Some works can fool detectors by crafting the adversarial camouflage attached to the object, leading to wrong prediction. It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance. Motivated by this, this paper proposes the Dual Attribute Adversarial Camouflage (DAAC) for evading the detection by both detectors and humans. Generating DAAC includes two steps: (1) Extracting features from a specific type of scene to generate individual soldier digital camouflage; (2) Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC. The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features. Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors, thus evading the detections by both detectors and humans in the digital domain. This work can serve as the reference for crafting the adversarial camouflage in the physical world.