Content-based Unrestricted Adversarial Attack
- URL: http://arxiv.org/abs/2305.10665v2
- Date: Wed, 29 Nov 2023 01:52:54 GMT
- Title: Content-based Unrestricted Adversarial Attack
- Authors: Zhaoyu Chen and Bo Li and Shuang Wu and Kaixun Jiang and Shouhong Ding
and Wenqiang Zhang
- Abstract summary: We propose a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack.
By leveraging a low-dimensional manifold that represents natural images, we map the images onto the manifold and optimize them along its adversarial direction.
- Score: 53.181920529225906
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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 deceive human
perception and deep neural networks with stealth and success. However, current
works usually sacrifice unrestricted degrees and subjectively select some image
content to guarantee the photorealism of unrestricted adversarial examples,
which limits its attack performance. To ensure the photorealism of adversarial
examples and boost attack performance, we propose a novel unrestricted attack
framework called Content-based Unrestricted Adversarial Attack. By leveraging a
low-dimensional manifold that represents natural images, we map the images onto
the manifold and optimize them along its adversarial direction. Therefore,
within this framework, we implement Adversarial Content Attack based on Stable
Diffusion and can generate high transferable unrestricted adversarial examples
with various adversarial contents. Extensive experimentation and visualization
demonstrate the efficacy of ACA, particularly in surpassing state-of-the-art
attacks by an average of 13.3-50.4% and 16.8-48.0% in normally trained models
and defense methods, respectively.
Related papers
- Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent
Diffusion Model [61.53213964333474]
We propose a unified framework Adv-Diffusion that can generate imperceptible adversarial identity perturbations in the latent space but not the raw pixel space.
Specifically, we propose the identity-sensitive conditioned diffusion generative model to generate semantic perturbations in the surroundings.
The designed adaptive strength-based adversarial perturbation algorithm can ensure both attack transferability and stealthiness.
arXiv Detail & Related papers (2023-12-18T15:25:23Z) - LFAA: Crafting Transferable Targeted Adversarial Examples with
Low-Frequency Perturbations [25.929492841042666]
We present a novel approach to generate transferable targeted adversarial examples.
We exploit the vulnerability of deep neural networks to perturbations on high-frequency components of images.
Our proposed approach significantly outperforms state-of-the-art methods.
arXiv Detail & Related papers (2023-10-31T04:54:55Z) - IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks [16.577595936609665]
We introduce a novel approach to counter adversarial attacks, namely, image resampling.
Image resampling transforms a discrete image into a new one, simulating the process of scene recapturing or rerendering as specified by a geometrical transformation.
We show that our method significantly enhances the adversarial robustness of diverse deep models against various attacks while maintaining high accuracy on clean images.
arXiv Detail & Related papers (2023-10-18T11:19:32Z) - Dual Adversarial Resilience for Collaborating Robust Underwater Image
Enhancement and Perception [54.672052775549]
In this work, we introduce a collaborative adversarial resilience network, dubbed CARNet, for underwater image enhancement and subsequent detection tasks.
We propose a synchronized attack training strategy with both visual-driven and perception-driven attacks enabling the network to discern and remove various types of attacks.
Experiments demonstrate that the proposed method outputs visually appealing enhancement images and perform averagely 6.71% higher detection mAP than state-of-the-art methods.
arXiv Detail & Related papers (2023-09-03T06:52:05Z) - Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face
Recognition [111.1952945740271]
Adversarial Attributes (Adv-Attribute) is designed to generate inconspicuous and transferable attacks on face recognition.
Experiments on the FFHQ and CelebA-HQ datasets show that the proposed Adv-Attribute method achieves the state-of-the-art attacking success rates.
arXiv Detail & Related papers (2022-10-13T09:56:36Z) - Shadows can be Dangerous: Stealthy and Effective Physical-world
Adversarial Attack by Natural Phenomenon [79.33449311057088]
We study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow.
We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments.
arXiv Detail & Related papers (2022-03-08T02:40:18Z) - Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations
with Perceptual Similarity [5.03315505352304]
Adversarial examples are malicious images with visually imperceptible perturbations.
We propose Demiguise Attack, crafting unrestricted'' perturbations with Perceptual Similarity.
We extend widely-used attacks with our approach, enhancing adversarial effectiveness impressively while contributing to imperceptibility.
arXiv Detail & Related papers (2021-07-03T10:14:01Z) - Adversarial Examples Detection beyond Image Space [88.7651422751216]
We find that there exists compliance between perturbations and prediction confidence, which guides us to detect few-perturbation attacks from the aspect of prediction confidence.
We propose a method beyond image space by a two-stream architecture, in which the image stream focuses on the pixel artifacts and the gradient stream copes with the confidence artifacts.
arXiv Detail & Related papers (2021-02-23T09:55:03Z) - Perception Improvement for Free: Exploring Imperceptible Black-box
Adversarial Attacks on Image Classification [27.23874129994179]
White-box adversarial attacks can fool neural networks with small perturbations, especially for large size images.
Keeping successful adversarial perturbations imperceptible is especially challenging for transfer-based black-box adversarial attacks.
We propose structure-aware adversarial attacks by generating adversarial images based on psychological perceptual models.
arXiv Detail & Related papers (2020-10-30T07:17:12Z) - Generating Semantic Adversarial Examples via Feature Manipulation [23.48763375455514]
We propose a more practical adversarial attack by designing structured perturbation with semantic meanings.
Our proposed technique manipulates the semantic attributes of images via the disentangled latent codes.
We demonstrate the existence of a universal, image-agnostic semantic adversarial example.
arXiv Detail & Related papers (2020-01-06T06:28:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.