Instruct2Attack: Language-Guided Semantic Adversarial Attacks
- URL: http://arxiv.org/abs/2311.15551v1
- Date: Mon, 27 Nov 2023 05:35:49 GMT
- Title: Instruct2Attack: Language-Guided Semantic Adversarial Attacks
- Authors: Jiang Liu, Chen Wei, Yuxiang Guo, Heng Yu, Alan Yuille, Soheil Feizi,
Chun Pong Lau, Rama Chellappa
- Abstract summary: Instruct2Attack (I2A) is a language-guided semantic attack that generates meaningful perturbations according to free-form language instructions.
We make use of state-of-the-art latent diffusion models, where we adversarially guide the reverse diffusion process to search for an adversarial latent code conditioned on the input image and text instruction.
We show that I2A can successfully break state-of-the-art deep neural networks even under strong adversarial defenses.
- Score: 76.83548867066561
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We propose Instruct2Attack (I2A), a language-guided semantic attack that
generates semantically meaningful perturbations according to free-form language
instructions. We make use of state-of-the-art latent diffusion models, where we
adversarially guide the reverse diffusion process to search for an adversarial
latent code conditioned on the input image and text instruction. Compared to
existing noise-based and semantic attacks, I2A generates more natural and
diverse adversarial examples while providing better controllability and
interpretability. We further automate the attack process with GPT-4 to generate
diverse image-specific text instructions. We show that I2A can successfully
break state-of-the-art deep neural networks even under strong adversarial
defenses, and demonstrate great transferability among a variety of network
architectures.
Related papers
- Natural Language Induced Adversarial Images [14.415478695871604]
We propose a natural language induced adversarial image attack method.
The core idea is to leverage a text-to-image model to generate adversarial images given input prompts.
In experiments, we found that some high-frequency semantic information such as "foggy", "humid", "stretching" can easily cause errors.
arXiv Detail & Related papers (2024-10-11T08:36:07Z) - DiffuseDef: Improved Robustness to Adversarial Attacks [38.34642687239535]
adversarial attacks pose a critical challenge to system built using pretrained language models.
We propose DiffuseDef, which incorporates a diffusion layer as a denoiser between the encoder and the classifier.
During inference, the adversarial hidden state is first combined with sampled noise, then denoised iteratively and finally ensembled to produce a robust text representation.
arXiv Detail & Related papers (2024-06-28T22:36:17Z) - Few-Shot Adversarial Prompt Learning on Vision-Language Models [62.50622628004134]
The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention.
Previous efforts achieved zero-shot adversarial robustness by aligning adversarial visual features with text supervision.
We propose a few-shot adversarial prompt framework where adapting input sequences with limited data makes significant adversarial robustness improvement.
arXiv Detail & Related papers (2024-03-21T18:28:43Z) - VL-Trojan: Multimodal Instruction Backdoor Attacks against
Autoregressive Visual Language Models [65.23688155159398]
Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning capabilities in a multimodal context.
Recently, multimodal instruction tuning has been proposed to further enhance instruction-following abilities.
Adversaries can implant a backdoor by injecting poisoned samples with triggers embedded in instructions or images.
We propose a multimodal instruction backdoor attack, namely VL-Trojan.
arXiv Detail & Related papers (2024-02-21T14:54:30Z) - AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large
Language Models [55.748851471119906]
Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks.
Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters.
We introduce AutoDAN, an interpretable, gradient-based adversarial attack that merges the strengths of both attack types.
arXiv Detail & Related papers (2023-10-23T17:46:07Z) - Language Guided Adversarial Purification [3.9931474959554496]
Adversarial purification using generative models demonstrates strong adversarial defense performance.
New framework, Language Guided Adversarial Purification (LGAP), utilizing pre-trained diffusion models and caption generators.
arXiv Detail & Related papers (2023-09-19T06:17:18Z) - Controlled Caption Generation for Images Through Adversarial Attacks [85.66266989600572]
We study adversarial examples for vision and language models, which typically adopt a Convolutional Neural Network (i.e., CNN) for image feature extraction and a Recurrent Neural Network (RNN) for caption generation.
In particular, we investigate attacks on the visual encoder's hidden layer that is fed to the subsequent recurrent network.
We propose a GAN-based algorithm for crafting adversarial examples for neural image captioning that mimics the internal representation of the CNN.
arXiv Detail & Related papers (2021-07-07T07:22:41Z) - A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems.
This paper proposes a self-supervised adversarial training mechanism in the input space.
It provides significant robustness against the textbfunseen adversarial attacks.
arXiv Detail & Related papers (2020-06-08T20:42:39Z) - 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.