Erase but Preserve: Controllable Removal of Copyrighted Animation Characters via Optimized Semantic Anchors

• URL: http://arxiv.org/abs/2608.12806v1
• Date: Thu, 13 Aug 2026 04:26:47 GMT
• Title: Erase but Preserve: Controllable Removal of Copyrighted Animation Characters via Optimized Semantic Anchors
• Abstract summary: We propose a controllable method operating on the model's continuous textual representation to erase target characters during generation.<n> Experiments show that our method achieves state-of-the-art erasure effectiveness and image fidelity preservation.<n>Our optimized anchors are plug-and-play with current model modification baselines to improve their erasure performance.
• Score: 21.93748761401005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exceptional generation capabilities of text-to-image diffusion models have raised copyright concerns, particularly the unauthorized reproduction of animation characters. Existing concept erasure methods fall short for animation character erasure: model modification methods struggle to identify suitable anchors for diverse, highly distinctive characters; prompt-based steering methods lack fine-grained control for precise intervention. These approaches often yield incomplete erasure and degraded image fidelity, hindering real-world deployment. In this paper, we propose a controllable method operating on the model's continuous textual representation to erase target characters during generation. We optimizes an anchor embedding via structural and detailed constraints to serve as a character surrogate, then replaces target-related embeddings with the anchor via a structure-aware adaptive strategy. Experiments show that our method achieves state-of-the-art erasure effectiveness and image fidelity preservation, while supporting controllable erasure degree, multi-target removal, and model transferability. Moreover, our optimized anchors are plug-and-play with current model modification baselines to improve their erasure performance.

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