FIDA: Feature Instability-Driven Attack on Self-Supervised Facial Representation

• URL: http://arxiv.org/abs/2608.26861v1
• Date: Thu, 27 Aug 2026 09:27:25 GMT
• Title: FIDA: Feature Instability-Driven Attack on Self-Supervised Facial Representation
• Abstract summary: We propose FIDA (Feature Instability-Driven Attack), a novel backdoor attack framework.<n>FIDA uses subtle semantic triggers for injection, but its key innovation is a novel objective called Feature Instability Loss.<n> Experiments show that FIDA achieves a high attack success rate and generally preserves benign utility across the evaluated settings.
• Score: 5.071273351499079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) models are vulnerable to backdoor attacks. However, the systemic risks they pose in face representation have received little attention. The entanglement of identity features in self-supervised face learning presents unique challenges for attack stealthiness. To address this gap, we propose FIDA (Feature Instability-Driven Attack), a novel backdoor attack framework. FIDA uses subtle semantic triggers for injection, but its key innovation is a novel objective called Feature Instability Loss. It trains the encoder to increase the sensitivity of triggered features along perturbation directions sampled during attack optimization . By preventing the backdoor from exhibiting the rigid feature patterns typical of previous attacks, FIDA effectively evades the evaluated perturbation-based defenses. Experiments show that FIDA achieves a high attack success rate and generally preserves benign utility across the evaluated settings , posing a significant threat to real-world multimedia applications relying on facial analysis.

Related papers

• Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming [64.48633529149579]
  We propose a novel framework to expose the vulnerabilities of Vision-Language-Action (VLA) models against linguistic variations.<n>Our method reduces the average task success rate from 93.33% to 5.85%, demonstrating a scalable approach to stress-testing VLA agents.
  arXiv  Detail & Related papers  (2026-04-07T08:43:36Z)
• Debiased Dual-Invariant Defense for Adversarially Robust Person Re-Identification [52.63017280231648]
  Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking.<n>Person ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions.<n>We propose a dual-invariant defense framework composed of two main phases.
  arXiv  Detail & Related papers  (2025-11-13T03:56:40Z)
• Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
  This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
  arXiv  Detail & Related papers  (2025-08-01T06:58:00Z)
• SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning [10.924427077035915]
  Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenges.<n>We propose a novel and stealthy backdoor attack framework, named SPA, which departs from traditional approaches by leveraging feature-space alignment.<n>Our results call urgent attention to the evolving sophistication of backdoor threats in FL and emphasize the pressing need for advanced, feature-level defense techniques.
  arXiv  Detail & Related papers  (2025-06-26T01:33:14Z)
• The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [101.68501850486179]
  We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities.<n>This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set.<n>We propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG.
  arXiv  Detail & Related papers  (2025-05-24T08:19:25Z)
• BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models [57.5404308854535]
  Safety backdoor attacks in large language models (LLMs) enable the stealthy triggering of unsafe behaviors while evading detection during normal interactions. We present BEEAR, a mitigation approach leveraging the insight that backdoor triggers induce relatively uniform drifts in the model's embedding space. Our bi-level optimization method identifies universal embedding perturbations that elicit unwanted behaviors and adjusts the model parameters to reinforce safe behaviors against these perturbations.
  arXiv  Detail & Related papers  (2024-06-24T19:29:47Z)
• Invisible Backdoor Attack Through Singular Value Decomposition [2.681558084723648]
  backdoor attacks pose a serious security threat to deep neural networks (DNNs) To make triggers less perceptible and imperceptible, various invisible backdoor attacks have been proposed. This paper proposes an invisible backdoor attack called DEBA.
  arXiv  Detail & Related papers  (2024-03-18T13:25:12Z)
• AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration [14.392609872661065]
  Deep learning-based face restoration models have become targets for sophisticated backdoor attacks.<n>We introduce a unique degradation objective tailored for attacking restoration models.<n>We propose the Adaptive Selective Frequency Injection Backdoor Attack (AS-FIBA) framework.
  arXiv  Detail & Related papers  (2024-03-11T04:44:26Z)
• Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks [41.531913152661296]
  We formulate the problem of targeted adversarial attack on Stable Diffusion and propose a framework to generate adversarial prompts. Specifically, we design a gradient-based embedding optimization method to craft reliable adversarial prompts that guide stable diffusion to generate specific images. After obtaining successful adversarial prompts, we reveal the mechanisms that cause the vulnerability of the model.
  arXiv  Detail & Related papers  (2024-01-16T12:15:39Z)
• LEAT: Towards Robust Deepfake Disruption in Real-World Scenarios via Latent Ensemble Attack [11.764601181046496]
  Deepfakes, malicious visual contents created by generative models, pose an increasingly harmful threat to society. To proactively mitigate deepfake damages, recent studies have employed adversarial perturbation to disrupt deepfake model outputs. We propose a simple yet effective disruption method called Latent Ensemble ATtack (LEAT), which attacks the independent latent encoding process.
  arXiv  Detail & Related papers  (2023-07-04T07:00:37Z)
• 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)

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.