"Mitigation and Identification of Camouflage Attack Over Computer Vision Applications"

Abstract

Computer vision technologies are now commonly used in real-time image and video recognition applications using deep neural networks. Scaling or Mitigation is the basic input pre-processing feature in these implementations. Image scaling methods are designed to maintain visual elements before and after scaling, and are widely utilized in a variety of visual and image processing applications. Content disguising attacks may be carried out by taking advantage of the picture scaling mechanism, which causes the machine's extracted content to be drastically different from the input before scaling. In this project the actual input image with dimension Am×n is resized to an attacked image dimension Om1×n1, which will mislead the classifier as the input is attacked image. To illustrate the threats from such camouflage attacks, some computer vision applications taken as targeted victims that includes multiple image classification applications based on popular deep learning frameworks like OpenCV, pillow. This attack is not detected while checking with the YOLOV4 demo. To defend against such attacks, in this project using Dhash i.e., difference hash concept. The hash the value of the actual image and the resized are same when using the Dhash. If the image is camouflaged the means the content is modified so the Dhash value of the image also changed if the image is not camouflaged the then the hash value of the actual image and resized images are same. By using the Dhash concept detecting the image is camouflaged or not.