Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
Thanks to recent advances in deep neural networks (DNNs), face recognition systems have become highly accurate in classifying a large number of face images. However, recent studies have found that DNNs could be vulnerable to adversarial examples, raising concerns about the robustness of such systems. Adversarial examples that are not restricted to small perturbations could be more serious since conventional certified defenses might be ineffective against them. To shed light on the vulnerability to such adversarial examples, we propose a flexible and efficient method for generating unrestricted adversarial examples using image translation techniques. Our method enables us to translate a source image into any desired facial appearance with large perturbations to deceive target face recognition systems. Our experimental results indicate that our method achieved about $90$ and $80\%$ attack success rates under white- and black-box settings, respectively, and that the translated images are perceptually realistic and maintain the identifiability of the individual while the perturbations are large enough to bypass certified defenses.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionTranslationSimilar Papers 제목 키워드 기반
Content-based Unrestricted Adversarial Attack
Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to de…
Adversarial AttackAdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models
Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass …
Adversarial AttackAdversarial DefenseConstructing Unrestricted Adversarial Examples with Generative Models
Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose u…
Generative Adversarial NetworkGenerating Unrestricted Adversarial Examples via Three Parameters
Deep neural networks have been shown to be vulnerable to adversarial examples deliberately constructed to misclassify victim models. As most adversarial examples have restricted their perturbations to $L_{p}$-norm, exist…
Adversarial AttackSynthesizing Unrestricted False Positive Adversarial Objects Using Generative Models
Adversarial examples are data points misclassified by neural networks. Originally, adversarial examples were limited to adding small perturbations to a given image. Recent work introduced the generalized concept of unres…
Objectobject-detectionObject Detection