Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms
Image scaling algorithms are intended to preserve the visual features before and after scaling, which is commonly used in numerous visual and image processing applications. In this paper, we demonstrate an automated attack against common scaling algorithms, i.e. to automatically generate camouflage images whose visual semantics change dramatically after scaling. To illustrate the threats from such camouflage attacks, we choose several computer vision applications as targeted victims, including multiple image classification applications based on popular deep learning frameworks, as well as main-stream web browsers. Our experimental results show that such attacks can cause different visual results after scaling and thus create evasion or data poisoning effect to these victim applications. We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud. To defend against such attacks, this paper suggests a few potential countermeasures from attack prevention to detection.
Code (1)
Tasks
Data Poisoningimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
As an essential processing step in computer vision applications, image resizing or scaling, more specifically downsampling, has to be applied before feeding a normally large image into a convolutional neural network (CNN…
CPUSteganalysisSeeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum
Recent advances in camouflaged object detection (COD) have led to substantial progress in challenging low-visibility scenarios, with pioneering studies demonstrating notable success in localizing objects in camouflaged s…
Object DetectionReading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons
With Open AI's publishing of their CLIP model (Contrastive Language-Image Pre-training), multi-modal neural networks now provide accessible models that combine reading with visual recognition. Their network offers novel …
Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present. In this work, we systematically invest…
Visual Question AnsweringIn-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing
Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. Among them, camouflage attacks manipulate an object's visible appearance to deceive det…
Image Editing