Anti-Customization

By Gillian_235

  • Fawkes

    USENIX《Fawkes: Protecting Privacy against Unauthorized Deep Learning Models》

  • UE

    ICLR 2021《Unlearnable Examples: Making Personal Data Unexploitable》

  • Glaze

    USENIX 2023《Glaze: Protecting artists from style mimicry by {Text-to-Image} models》

  • AdvDM

    ICML 2023《Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples》
    Estimate adversarial examples for DMs by optimizing
    upon different latent variables sampled from the
    reverse process of DMs.

  • PhotoGuard

    《Raising the Cost of Malicious AI-Powered Image Editing》

  • Anti-DreamBooth

    ICCV 2023《Anti-DreamBooth: Protecting Users from Personalized Text-to-image Synthesis》

  • SimAC

    CVPR 2024《SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models》

  • ImageShield

    Springer《ImageShield: a responsibility-to-person blind watermarking mechanism for image datasets protection》
    Combine traditional transform domain watermarking with an enhanced GAN.