Let the Carrier Carry the Attack: Preserving the Subject in Adversarial Image Generation
Not on the map. Why?
Abstract (arXiv)
Strong unrestricted adversarial attacks can distort the primary object of an image, hereafter referred to as the subject. To preserve subject integrity without compromising attack magnitude, we introduce the carrier: a secondary visual element that provides an auxiliary region to facilitate the attack under global classifier guidance. We demonstrate three key findings: 1. A carrier mitigates subject distortion by absorbing a larger share of globally normalized attack updates. 2. A carrier improves cross-model transferability, governed by the strength of target-related features that balance semantic separation and transfer performance. 3. Successful targeted attacks retain the personalized subject as the primary content perceived by humans while successfully misleading the classifier. Our results demonstrate that a visually secondary carrier offers an auxiliary spatial pathway for adversarial changes, enabling strong and transferable attacks while improving subject preservation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 30 Sep 2026.
Builds on
19 of 26 references.
References · 19
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2013Intriguing properties of neural networkscited by 5,155
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2019Root Mean Square Layer Normalizationcited by 1,480
- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
- 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Spacecited by 1,006
- 2018Constructing Unrestricted Adversarial Examples with Generative Modelscited by 135
- 2021Feature Importance-aware Transferable Adversarial Attackscited by 127
- 2023Diffusion Models for Imperceptible and Transferable Adversarial Attackcited by 94
- 2023Content-based Unrestricted Adversarial Attackcited by 73
7 more
- 2022A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributescited by 56
- 2023AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Modelscited by 46
- 2018Unrestricted Adversarial Examplescited by 40
- 2023Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustnesscited by 20
- 2025NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusioncited by 6
- 2024ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changescited by 5
- 2025Concept-based Adversarial Attack: a Probabilistic Perspectivecited by 2
Built on by
Not cited yet.