Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline
Deep learning technologies have become the backbone for the development of computer vision. With further explorations, deep neural networks have been found vulnerable to well-designed adversarial attacks. Most of the vision devices are equipped with image signal processing (ISP) pipeline to implement RAW-to-RGB transformations and embedded into data preprocessing module for efficient image processing. Actually, ISP pipeline can introduce adversarial behaviors to post-capture images while data preprocessing may destroy attack patterns. However, none of the existing adversarial attacks takes into account the impacts of both ISP pipeline and data preprocessing. In this paper, we develop an image-scaling attack targeting on ISP pipeline, where the crafted adversarial RAW can be transformed into attack image that presents entirely different appearance once being scaled to a specific-size image. We first consider the gradient-available ISP pipeline, i.e., the gradient information can be directly used in the generation process of adversarial RAW to launch the attack. To make the adversarial attack more applicable, we further consider the gradient-unavailable ISP pipeline, in which a proxy model that well learns the RAW-to-RGB transformations is proposed as the gradient oracles. Extensive experiments show that the proposed adversarial attacks can craft adversarial RAW data against the target ISP pipelines with high attack rates.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackSimilar Papers 제목 키워드 기반
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
Recent advancements in deep learning, particularly in medical imaging, have significantly propelled the progress of healthcare systems. However, examining the robustness of medical images against adversarial attacks is c…
Adversarial AttackAdversarial Attack DetectionDiagnosticDefending against adversarial attacks on medical imaging AI system, classification or detection?
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adversarial training and/or loss function b…
Adversarial DefenseGeneral ClassificationRobustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging. In contrary to the defense methods against adversarial attacks for classification models…
Deep LearningSegmentationSecuring the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks
Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its capacity to handle vast, complicated, an…
Adversarial AttackMedical Image AnalysisExploring adversarial attacks in federated learning for medical imaging
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical…
Federated LearningMedical Image AnalysisPrivacy Preserving