mlsanityData to 7 Oct 2026

Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack

arXiv 2401.09673 ↗First posted 18 Jan 2024cs.CV cs.CR cs.LGcited by 9

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Abstract (arXiv)

Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists' rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), empowering artists to protect their artwork from unauthorized style transfer by processing before public release. By delving into the intricacies of human visual perception and the role of different frequency components, our method strategically introduces frequency-adaptive perturbations in the image. These perturbations significantly degrade the generation quality of NST while maintaining an acceptable level of visual change in the original image, ensuring that potential infringers are discouraged from using the protected artworks, because of its bad NST generation quality. Additionally, existing metrics often overlook the importance of color fidelity in evaluating color-mattered tasks, such as the quality of NST-generated images, which is crucial in the context of artistic works. To comprehensively assess the color-mattered tasks, we propose the Adversarial Color Distance Metric (ACDM), designed to quantify the color difference of images pre- and post-manipulations. Experimental results confirm that attacking NST using LAACA results in visually inferior style transfer, and the ACDM can efficiently measure color-mattered tasks. By providing artists with a tool to safeguard their intellectual property, our work relieves the socio-technical challenges posed by the misuse of NST in the art community.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 38 of its 60 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

38 of 60 references.

References · 38

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  7. 2013Intriguing properties of neural networkscited by 5,114
  8. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  9. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  10. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  11. 2016Universal adversarial perturbationscited by 774
  12. 2015A Neural Algorithm of Artistic Stylecited by 480
26 more
  1. 2017Universal Style Transfer via Feature Transformscited by 352
  2. 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
  3. 2017Deep Photo Style Transfercited by 168
  4. 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
  5. 2017On the Robustness of Semantic Segmentation Models to Adversarial Attackscited by 126
  6. 2022Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalizationcited by 122
  7. 2023Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Modelscited by 87
  8. 2018Low Frequency Adversarial Perturbationcited by 80
  9. 2021Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer visioncited by 76
  10. 2017Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Lossescited by 69
  11. 2021Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detectioncited by 67
  12. 2019FDA: Feature Disruptive Attackcited by 55
  13. 2024Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learningcited by 47
  14. 2022Exploring Frequency Adversarial Attacks for Face Forgery Detectioncited by 45
  15. 2019A Closed-form Solution to Universal Style Transfercited by 42
  16. 2021Harnessing Perceptual Adversarial Patches for Crowd Countingcited by 39
  17. 2018Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Modelcited by 33
  18. 2023Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognitioncited by 33
  19. 2023Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognitioncited by 32
  20. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  21. 2022Restricted Black-box Adversarial Attack Against DeepFake Face Swappingcited by 25
  22. 2022Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learningcited by 21
  23. 2016Preserving Color in Neural Artistic Style Transfercited by 20
  24. 2023A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Modelscited by 15
  25. 2022Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognitioncited by 11
  26. 2023LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbationscited by 8

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 4.

9 papers.

  1. 2024A Survey of Recent Backdoor Attacks and Defenses in Large Language Modelscited by 36
  2. 2024Instant Adversarial Purification with Adversarial Consistency Distillationcited by 14
  3. 2024A Gray-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapsecited by 13
  4. 2025My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swappingcited by 4
  5. 2025Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systemscited by 4
  6. 2025Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigmcited by 4
  7. 2024Breaking PEFT Limitations: Leveraging Weak-to-Strong Knowledge Transfer for Backdoor Attacks in LLMscited by 2
  8. 2025Towards more transferable adversarial attack in black-box mannercited by 2
  9. 2024StyleMark: A Robust Watermarking Method for Art Style Images Against Black-Box Arbitrary Style Transfercited by 0