pith. sign in

arxiv: 2606.09909 · v1 · pith:KRFBPAN2new · submitted 2026-06-06 · 💻 cs.CR · cs.AI· cs.CV

Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization

Ziang Xu , Wenbo Yu , Hongyao Yu , Hao Fang , Jiawei Kong , Bin Chen , Hao Wu , Shu-Tao Xia
show 1 more author
Zhiyong Wu
This is my paper

Pith reviewed 2026-06-27 19:38 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.CV
keywords diffusion modelscopyright protectionadversarial attackslatent spaceimage customizationdefense bypasspersonalized generation
0
0 comments X

The pith

Two-stage latent feature optimization restores disrupted mappings to bypass copyright defenses in diffusion models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces TS-LFO to bypass copyright protections added to diffusion-based image customization. Current defenses work by injecting persistent perturbations into the latent space of latent diffusion models, which breaks the mapping from input images to usable latent codes and prevents personalized outputs. TS-LFO counters the disruption in two stages: first by jointly minimizing a Latent-Image Alignment Loss and a Latent Diffusion Loss with timestep-dependent weights to suppress high-frequency noise, then by applying pixel-level constraints to recover low-frequency semantic details. A sympathetic reader would care because the work shows that static latent perturbations are not sufficient to stop adaptive adversaries from restoring the necessary mappings.

Core claim

Existing defenses primarily disrupt the mapping between input images and their latent representations in latent diffusion models. TS-LFO restores this mapping through a two-stage optimization: the Latent Denoising Stage enhances semantic consistency by minimizing the Latent-Image Alignment Loss and Latent Diffusion Loss with timestep-dependent weights to suppress high-frequency noise from defenses, while the Latent Reconstruction Stage recovers low-frequency semantic information using pixel-level constraints to refine the latent features, enabling the production of personalized outputs despite the protections.

What carries the argument

Two-Stage Latent Feature Optimization (TS-LFO), which performs a Latent Denoising Stage followed by a Latent Reconstruction Stage to restore the input-to-latent mapping broken by defense perturbations.

If this is right

  • TS-LFO consistently bypasses state-of-the-art copyright defenses.
  • TS-LFO outperforms prior attacks such as DiffPure, GrIDPure, and IMPRESS across diverse settings.
  • The two-stage process enables effective copyright-stealing attacks on protected diffusion-based customization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If defenses stay fixed, comparable optimization techniques could potentially transfer to other latent generative models.
  • Defense designers may need to make perturbations responsive to multi-stage restoration attempts to stay ahead.
  • Evaluating the method on additional diffusion architectures would test how broadly the mapping-restoration approach applies.

Load-bearing premise

Existing defenses primarily disrupt the mapping between input images and their latent representations in a manner that can be systematically restored by minimizing the described Latent-Image Alignment Loss and Latent Diffusion Loss without the defenses adapting to the attack.

What would settle it

A defense that is updated to detect and block the specific two-stage optimization process, after which TS-LFO no longer succeeds in restoring usable latent codes for personalized generation.

Figures

Figures reproduced from arXiv: 2606.09909 by Bin Chen, Hao Fang, Hao Wu, Hongyao Yu, Jiawei Kong, Shu-Tao Xia, Wenbo Yu, Zhiyong Wu, Ziang Xu.

Figure 1
Figure 1. Figure 1: An illustration of a copyright attack and defence [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of the reconstructed image 𝑥𝑟𝑒𝑐 = 𝐷(𝑧) and its corresponding input image. complementary set of metrics 𝑀 ∈ {𝐿𝑃𝐼𝑃𝑆, 𝑀𝑆𝐸, 𝑆𝑆𝐼𝑀,𝐶𝐿𝐼𝑃} for comprehensive evaluation, where an increase in LPIPS (perceptual difference) and MSE (mean squared error) signifies a larger dis￾crepancy, while a decrease in SSIM (structural similarity) and CLIP (semantic similarity) scores also indicates a larger discrepancy. … view at source ↗
Figure 3
Figure 3. Figure 3: The impact of latent feature 𝑧 on the L𝐿𝐷𝑀 under the influence of different copyright protections. the subsequent training process of the Unet denoiser. In diffusion model training, the objective of the Unet is to predict the added noise based on a noisy latent feature. When the input training sample is the semantically inconsistent 𝑧, we hypothesize that it provides contradictory and unstable learning sig… view at source ↗
Figure 5
Figure 5. Figure 5: We train Textual Inversion with three image sets [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualized comparison of images generated by different copyright attacks with the original images under the copyright [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

With the growing concerns over copyright infringement in diffusion-based customization, adversarial attacks have emerged as a prominent defense strategy to prevent malicious content forgery in personalized image generation. However, current defenses typically introduce persistent perturbations in the latent space of Latent Diffusion Models (LDMs), which remain susceptible to adaptive bypasses by adversaries. In this paper, we introduce Two-Stage Latent Feature Optimization (TS-LFO), an efficient and effective copyright-stealing attack against protected diffusion-based customization. We begin by observing that existing defenses primarily disrupt the mapping between input images and their latent representations, thereby degrading the model's ability to produce personalized outputs. To counteract this, TS-LFO restores the broken mapping through a two-stage optimization process. In the Latent Denoising Stage, we enhance semantic consistency between latent codes and input images by jointly minimizing a Latent-Image Alignment Loss and a Latent Diffusion Loss with timestep-dependent weights, effectively suppressing the high-frequency noise introduced by defenses. In the Latent Reconstruction Stage, we recover low-frequency semantic information using pixel-level constraints to refine the latent features. Extensive experiments show that TS-LFO consistently bypasses state-of-the-art (SOTA) copyright defenses and outperforms SOTA copyright attacks such as DiffPure, GrIDPure and IMPRESS across diverse settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes Two-Stage Latent Feature Optimization (TS-LFO) to bypass copyright defenses in latent diffusion models for personalized image generation. It observes that defenses disrupt input-to-latent mappings and counters this via a Latent Denoising Stage (jointly minimizing Latent-Image Alignment Loss and timestep-weighted Latent Diffusion Loss to suppress high-frequency noise) followed by a Latent Reconstruction Stage (pixel-level constraints to recover low-frequency semantics). The abstract asserts that extensive experiments demonstrate consistent bypass of SOTA defenses and outperformance over DiffPure, GrIDPure, and IMPRESS across settings.

Significance. If the empirical results hold under the stated conditions, the work provides concrete evidence that existing adversarial perturbations in LDM latent spaces are invertible via the described two-stage losses, underscoring the need for adaptive or loss-aware defenses in copyright protection for diffusion customization. The explicit construction of the losses and the two-stage schedule constitute a clear, testable attack strategy.

major comments (2)
  1. [Abstract] Abstract and §3 (method description): the bypass claim is load-bearing on the assumption that defenses (DiffPure, GrIDPure, IMPRESS) remain fixed and do not incorporate knowledge of the Latent-Image Alignment Loss or the two-stage schedule; no experiments are described that test against defenses adapted to penalize these exact terms or alter the noise schedule accordingly.
  2. [Abstract] The manuscript provides no quantitative tables or figures in the supplied abstract, and the central outperformance claim cannot be evaluated without the reported metrics, datasets, and defense configurations; this prevents assessment of whether the two-stage optimization actually restores the mapping more reliably than baselines.
minor comments (1)
  1. Notation for the timestep-dependent weights and loss coefficients should be defined explicitly with their functional forms rather than left as free parameters.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and §3 (method description): the bypass claim is load-bearing on the assumption that defenses (DiffPure, GrIDPure, IMPRESS) remain fixed and do not incorporate knowledge of the Latent-Image Alignment Loss or the two-stage schedule; no experiments are described that test against defenses adapted to penalize these exact terms or alter the noise schedule accordingly.

    Authors: Our evaluation targets the published, fixed implementations of the cited SOTA defenses. The results establish that these existing protections are invertible under the proposed two-stage losses. Adaptive defenses that explicitly penalize the Latent-Image Alignment Loss or modify the noise schedule are not present in the current literature; constructing and evaluating such defenses constitutes a separate research direction. We will add a brief limitations paragraph acknowledging this scope in the revision. revision: partial

  2. Referee: [Abstract] The manuscript provides no quantitative tables or figures in the supplied abstract, and the central outperformance claim cannot be evaluated without the reported metrics, datasets, and defense configurations; this prevents assessment of whether the two-stage optimization actually restores the mapping more reliably than baselines.

    Authors: Abstracts are length-constrained and conventionally omit tables and figures. The full manuscript supplies the requested details in Section 4 (Tables 1–3 and Figures 3–6), reporting bypass rates, FID, LPIPS, and CLIP scores on CelebA-HQ and FFHQ under the exact defense configurations. We will revise the abstract to include two or three key numerical results (e.g., average bypass success rate and margin over baselines) to improve immediate evaluability. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical attack method with independent experimental validation

full rationale

The paper describes an empirical two-stage optimization attack (TS-LFO) using Latent-Image Alignment Loss and timestep-weighted Latent Diffusion Loss, evaluated experimentally against external defenses (DiffPure, GrIDPure, IMPRESS). No equations, parameters, or claims reduce by construction to fitted inputs or self-citations. The central bypass result is supported by reported experiments rather than any self-referential derivation. This is a standard non-circular empirical security paper.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The attack rests on the domain assumption that defenses act mainly by breaking image-to-latent mappings and that this breakage is reversible via the proposed losses; several optimization hyperparameters (timestep weights, loss coefficients) are introduced without independent justification.

free parameters (2)
  • timestep-dependent weights
    Used to balance Latent-Image Alignment Loss and Latent Diffusion Loss during denoising stage.
  • loss coefficients for pixel-level constraints
    Introduced in the reconstruction stage to recover low-frequency information.
axioms (1)
  • domain assumption Existing defenses primarily disrupt the mapping between input images and their latent representations
    Stated as the starting observation that motivates the two-stage recovery process.

pith-pipeline@v0.9.1-grok · 5783 in / 1256 out tokens · 15450 ms · 2026-06-27T19:38:09.871938+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

45 extracted references · 9 canonical work pages · 4 internal anchors

  1. [1]

    Blattmann, R

    A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis,Align your latents: High-resolution video synthesis with latent diffusion models, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 22563–22575

    2023

  2. [2]

    B. Cao, C. Li, T. W ang, J. Jia, B. Li, and J. Chen,Impress: Evaluating the resilience of imperceptible perturbations against unauthorized data usage in diffusion-based generative ai, Advances in Neural Information Processing Systems, 36 (2023), pp. 10657–10677

    2023

  3. [3]

    H. Cao, C. Tan, Z. Gao, Y. Xu, G. Chen, P.-A. Heng, and S. Z. Li,A survey on gen- erative diffusion models, IEEE Transactions on Knowledge and Data Engineering, (2024)

    2024

  4. [4]

    B. Chen, W. Yu, Q. Zhang, T. Zhuang, H. Wu, Y. Jiang, and S.-T. Xia,Editable- deepsc: Reliable cross-modal semantic communications for facial editing, arXiv preprint arXiv:2411.15702, (2024)

    work page arXiv 2024

  5. [5]

    Croitoru, V

    F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah,Diffusion models in vision: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (2023), pp. 10850–10869

    2023

  6. [6]

    J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou,Retinaface: Single-shot multi-level face localisation in the wild, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5203–5212

    2020

  7. [7]

    J. Deng, J. Guo, N. Xue, and S. Zafeiriou,Arcface: Additive angular margin loss for deep face recognition, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4690–4699

    2019

  8. [8]

    Esser, J

    P. Esser, J. Chiu, P. Atighehchian, J. Granskog, and A. Germanidis,Structure and content-guided video synthesis with diffusion models, in Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 7346–7356

    2023

  9. [9]

    H. Fang, B. Chen, X. W ang, Z. W ang, and S.-T. Xia,Gifd: A generative gra- dient inversion method with feature domain optimization, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4967–4976

    2023

  10. [10]

    H. Fang, J. Kong, B. Chen, T. Dai, H. Wu, and S.-T. Xia,Clip-guided generative networks for transferable targeted adversarial attacks, in European Conference on Computer Vision, Springer, 2024, pp. 1–19

    2024

  11. [11]

    H. Fang, J. Kong, W. Yu, B. Chen, J. Li, H. Wu, S.-T. Xia, and K. Xu,One perturbation is enough: On generating universal adversarial perturbations against vision-language pre-training models, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 4090–4100

    2025

  12. [12]

    H. Fang, Y. Qiu, H. Yu, W. Yu, J. Kong, B. Chong, B. Chen, X. W ang, S.-T. Xia, and K. Xu,Privacy leakage on dnns: A survey of model inversion attacks and defenses, arXiv preprint arXiv:2402.04013, (2024)

    work page arXiv 2024

  13. [13]

    H. Fang, W. Yu, B. Chen, X. W ang, S.-T. Xia, Q. Liao, and K. Xu,Enhancing gra- dient inversion attacks in federated learning via hierarchical feature optimization, arXiv preprint arXiv:2604.00955, (2026)

    work page arXiv 2026

  14. [14]

    R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-or,An image is worth one word: Personalizing text-to-image generation using textual inversion, in The Eleventh International Conference on Learning Representations

  15. [15]

    R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or,An image is worth one word: Personalizing text-to-image generation using textual inversion, arXiv preprint arXiv:2208.01618, (2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  16. [16]

    Heusel, H

    M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, Gans trained by a two time-scale update rule converge to a local nash equilibrium, Advances in neural information processing systems, 30 (2017)

    2017

  17. [17]

    Progressive Growing of GANs for Improved Quality, Stability, and Variation

    T. Karras, T. Aila, S. Laine, and J. Lehtinen,Progressive growing of gans for improved quality, stability, and variation, arXiv preprint arXiv:1710.10196, (2017)

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  18. [18]

    Kawar, S

    B. Kawar, S. Zada, O. Lang, O. Tov, H. Chang, T. Dekel, I. Mosseri, and M. Irani,Imagic: Text-based real image editing with diffusion models, in Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 6007–6017

    2023

  19. [19]

    Adversarial example does good: Preventing painting im- itation from diffusion models via adversarial examples,

    C. Liang, X. Wu, Y. Hua, J. Zhang, Y. Xue, T. Song, Z. Xue, R. Ma, and H. Guan, Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples, arXiv preprint arXiv:2302.04578, (2023)

    work page arXiv 2023

  20. [20]

    X. Liu, D. H. Park, S. Azadi, G. Zhang, A. Chopikyan, Y. Hu, H. Shi, A. Rohrbach, and T. Darrell,More control for free! image synthesis with se- mantic diffusion guidance, in Proceedings of the IEEE/CVF winter conference on KDD ’26, August 09–13, 2026, Jeju Island, Republic of Korea Ziang Xu et al. applications of computer vision, 2023, pp. 289–299

    2026

  21. [21]

    Y. Liu, J. An, W. Zhang, D. Wu, J. Gu, Z. Lin, and W. W ang,Disrupting diffu- sion: Token-level attention erasure attack against diffusion-based customization, in Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 3587–3596

    2024

  22. [22]

    W. Nie, B. Guo, Y. Huang, C. Xiao, A. V ahdat, and A. Anandkumar,Diffu- sion models for adversarial purification, in International Conference on Machine Learning, PMLR, 2022, pp. 16805–16827

    2022

  23. [23]

    DreamFusion: Text-to-3D using 2D Diffusion

    B. Poole, A. Jain, J. T. Barron, and B. Mildenhall,Dreamfusion: Text-to-3d using 2d diffusion, arXiv preprint arXiv:2209.14988, (2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  24. [24]

    R. Qiao, Q. Tan, P. Y ang, Y. W ang, X. W ang, E. W an, G. Dong, S. Lang, S. Zhou, Y. Xu, Y. Zeng, J. W ang, C. Sun, C. Li, and H. Zhang,We-math 2.0: A ver- satile mathbook system for incentivizing visual mathematical reasoning, in The Fourteenth International Conference on Learning Representations, 2026

    2026

  25. [25]

    Y. Qiu, H. Fang, H. Yu, B. Chen, M. Qiu, and S.-T. Xia,A closer look at gan priors: Exploiting intermediate features for enhanced model inversion attacks, in European Conference on Computer Vision, Springer, 2024, pp. 109–126

    2024

  26. [26]

    Y. Qiu, H. Yu, H. Fang, W. Yu, B. Chen, X. W ang, S.-T. Xia, and K. Xu,Mibench: A comprehensive benchmark for model inversion attack and defense, (2024)

    2024

  27. [27]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al.,Learning transferable visual models from natural language supervision, in International conference on machine learning, PmLR, 2021, pp. 8748–8763

    2021

  28. [28]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer,High-resolution image synthesis with latent diffusion models, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10684–10695

    2022

  29. [29]

    Ronneberger, P

    O. Ronneberger, P. Fischer, and T. Brox,U-net: Convolutional networks for biomedical image segmentation, in Medical image computing and computer- assisted intervention–MICCAI 2015: 18th international conference, Munich, Ger- many, October 5-9, 2015, proceedings, part III 18, Springer, 2015, pp. 234–241

    2015

  30. [30]

    N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman,Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 22500–22510

    2023

  31. [31]

    Raising the cost of malicious ai-powered image editing.arXiv preprint arXiv:2302.06588, 2023

    H. Salman, A. Khaddaj, G. Leclerc, A. Ilyas, and A. Madry,Raising the cost of malicious ai-powered image editing, arXiv preprint arXiv:2302.06588, (2023)

    work page arXiv 2023

  32. [32]

    Takagi and S

    Y. Takagi and S. Nishimoto,High-resolution image reconstruction with latent diffusion models from human brain activity, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14453–14463

    2023

  33. [33]

    Y. Tan, Y. Peng, H. Fang, B. Chen, and S.-T. Xia,Waterdiff: Perceptual image wa- termarks via diffusion model, in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2024, pp. 3250–3254

    2024

  34. [34]

    V an Le, H

    T. V an Le, H. Phung, T. H. Nguyen, Q. Dao, N. N. Tran, and A. Tran,Anti- dreambooth: Protecting users from personalized text-to-image synthesis, in Pro- ceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 2116–2127

    2023

  35. [35]

    W ang, Z

    F. W ang, Z. Tan, T. Wei, Y. Wu, and Q. Huang,Simac: A simple anti-customization method for protecting face privacy against text-to-image synthesis of diffusion models, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 12047–12056

    2024

  36. [36]

    W ang, C

    Z. W ang, C. Chen, L. Lyu, D. N. Metaxas, and S. Ma,Diagnosis: Detecting unau- thorized data usages in text-to-image diffusion models, in The Twelfth International Conference on Learning Representations

  37. [37]

    H. Xiao, W. Yu, H. Fang, S. Sun, B. Chen, X. W ang, and S.-T. Xia,Diffusion-based natural adversarial perturbations towards segment anything model, in ICASSP 2026- 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2026, pp. 13637–13641

    2026

  38. [38]

    Y ang, Z

    L. Y ang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Y ang,Diffusion models: A comprehensive survey of methods and applications, ACM Computing Surveys, 56 (2023), pp. 1–39

    2023

  39. [39]

    F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao,Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop, arXiv preprint arXiv:1506.03365, (2015)

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  40. [40]

    W. Yu, B. Chen, Q. Zhang, and S.-T. Xia,Editable-deepsc: cross-modal editable semantic communication systems, in 2024 IEEE 99th Vehicular Technology Con- ference (VTC2024-Spring), IEEE, 2024, pp. 1–5

    2024

  41. [41]

    W. Yu, H. Fang, B. Chen, X. Sui, C. Chen, H. Wu, S.-T. Xia, and K. Xu,Gi-nas: Boosting gradient inversion attacks through adaptive neural architecture search, IEEE Transactions on Information Forensics and Security, (2025)

    2025

  42. [42]

    Zhang, P

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. W ang,The unreasonable effectiveness of deep features as a perceptual metric, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595

    2018

  43. [43]

    Zhang,A better autoencoder for image: Convolutional autoencoder, in ICONIP17-DCEC

    Y. Zhang,A better autoencoder for image: Convolutional autoencoder, in ICONIP17-DCEC. Available online: http://users. cecs. anu. edu. au/Tom. Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58. pdf (accessed on 23 March 2017), 2018

    2017

  44. [44]

    Zhang, Y

    Y. Zhang, Y. Huang, Y. Sun, C. Liu, Z. Zhao, Z. Fang, Y. W ang, H. Chen, X. Y ang, X. Wei, et al.,Multitrust: A comprehensive benchmark towards trustworthy multi- modal large language models, Advances in Neural Information Processing Systems, 37 (2024), pp. 49279–49383

    2024

  45. [45]

    Which images are of the best quality and most similar to the reference (clean) image? Please select 2 to 4 images

    Z. Zhao, J. Duan, K. Xu, C. W ang, R. Zhang, Z. Du, Q. Guo, and X. Hu,Can protective perturbation safeguard personal data from being exploited by stable diffusion?, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 24398–24407. A Algorithm of our method Our TS-LFO framework is outlined in Fig. 4. While the mai...

    2024