TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image
  Diffusion Models
Recent advances in text-to-image diffusion models enable photorealistic image
generation, but they also risk producing malicious content, such as NSFW
images. To mitigate risk, concept erasure methods are studied to facilitate the
model to unlearn specific concepts. However, current studies struggle to fully
erase malicious concepts implicitly embedded in prompts (e.g., metaphorical
expressions or adversarial prompts) while preserving the model's normal
generation capability. To address this challenge, our study proposes TRCE,
using a two-stage concept erasure strategy to achieve an effective trade-off
between reliable erasure and knowledge preservation. Firstly, TRCE starts by
erasing the malicious semantics implicitly embedded in textual prompts. By
identifying a critical mapping objective(i.e., the [EoT] embedding), we
optimize the cross-attention layers to map malicious prompts to contextually
similar prompts but with safe concepts. This step prevents the model from being
overly influenced by malicious semantics during the denoising process.
Following this, considering the deterministic properties of the sampling
trajectory of the diffusion model, TRCE further steers the early denoising
prediction toward the safe direction and away from the unsafe one through
contrastive learning, thus further avoiding the generation of malicious
content. Finally, we conduct comprehensive evaluations of TRCE on multiple
malicious concept erasure benchmarks, and the results demonstrate its
effectiveness in erasing malicious concepts while better preserving the model's
original generation ability. The code is available at:
http://github.com/ddgoodgood/TRCE. CAUTION: This paper includes model-generated
content that may contain offensive material.
67d03821fe8f90f34f04973d
Ruidong Chen
claimed_verified
2025-03-11T13:42:30.572Z
6602650e1737e5cd4a8367a6
/avatars/6c9271879739364fce22ece91412451b.svg
chendongdong
user
ddgoodgood
67d03821fe8f90f34f04973e
Honglin Guo
67d03821fe8f90f34f04973f
Lanjun Wang
67d03821fe8f90f34f049740
Chenyu Zhang
67d03821fe8f90f34f049741
Weizhi Nie
67d03821fe8f90f34f049742
An-An Liu
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image
  Diffusion Models
Recent advances in text-to-image diffusion models enable photorealistic image
generation, but they also risk producing malicious content, such as NSFW
images. To mitigate risk, concept erasure methods are studied to facilitate the
model to unlearn specific concepts. However, current studies struggle to fully
erase malicious concepts implicitly embedded in prompts (e.g., metaphorical
expressions or adversarial prompts) while preserving the model's normal
generation capability. To address this challenge, our study proposes TRCE,
using a two-stage concept erasure strategy to achieve an effective trade-off
between reliable erasure and knowledge preservation. Firstly, TRCE starts by
erasing the malicious semantics implicitly embedded in textual prompts. By
identifying a critical mapping objective(i.e., the [EoT] embedding), we
optimize the cross-attention layers to map malicious prompts to contextually
similar prompts but with safe concepts. This step prevents the model from being
overly influenced by malicious semantics during the denoising process.
Following this, considering the deterministic properties of the sampling
trajectory of the diffusion model, TRCE further steers the early denoising
prediction toward the safe direction and away from the unsafe one through
contrastive learning, thus further avoiding the generation of malicious
content. Finally, we conduct comprehensive evaluations of TRCE on multiple
malicious concept erasure benchmarks, and the results demonstrate its
effectiveness in erasing malicious concepts while better preserving the model's
original generation ability. The code is available at:
http://github.com/ddgoodgood/TRCE. CAUTION: This paper includes model-generated
content that may contain offensive material.
TRCE employs a two-stage strategy to effectively erase malicious concepts from text-to-image diffusion models while preserving normal generation capabilities.
diffusion models text-to-image photorealistic image generation concept erasure malicious content NSFW images cross-attention layers denoising process contrastive learning sampling trajectory metaphorical expressions adversarial prompts