TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images (2411.00355v2)

Abstract: In this paper, we propose TextDestroyer, the first training- and annotation-free method for scene text destruction using a pre-trained diffusion model. Existing scene text removal models require complex annotation and retraining, and may leave faint yet recognizable text information, compromising privacy protection and content concealment. TextDestroyer addresses these issues by employing a three-stage hierarchical process to obtain accurate text masks. Our method scrambles text areas in the latent start code using a Gaussian distribution before reconstruction. During the diffusion denoising process, self-attention key and value are referenced from the original latent to restore the compromised background. Latent codes saved at each inversion step are used for replacement during reconstruction, ensuring perfect background restoration. The advantages of TextDestroyer include: (1) it eliminates labor-intensive data annotation and resource-intensive training; (2) it achieves more thorough text destruction, preventing recognizable traces; and (3) it demonstrates better generalization capabilities, performing well on both real-world scenes and generated images.

• The paper presents a novel diffusion-based method that requires no additional training or annotations for text removal.
• It introduces a three-stage hierarchical approach using attention maps and Gaussian noise to precisely obliterate text from images.
• Experimental results demonstrate effective text elimination with realistic background restoration, offering a new pathway for digital privacy protection.

TextDestroyer: A Novel Diffusion-Based Method for Text Destruction

The paper "TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images" presents a pioneering approach for text obliteration in images using a pre-trained diffusion model. This research is motivated by the prevalent issue of privacy concerns and unwanted text distributions in both real and synthesized digital imagery. Unlike existing models that necessitate labor-intensive annotation and complex retraining, TextDestroyer innovates with a training- and annotation-free method, offering a compelling alternative pathway in the domain of text removal.

The core contribution lies in its hierarchical text localization and destruction framework, effectively obliterating text through a systematic Gaussian-noise scrambling methodology while ensuring high fidelity in background restoration. This entropy-based approach does not rely on new data annotations or additional training, leveraging existing diffusion models' pre-trained capabilities to maintain efficiency and accessibility in practical deployment.

Methodological Overview

TextDestroyer employs a three-stage hierarchical approach in localized text identification:

1. Introductory Text Capturing: The method uses attention maps from diffusion model inversions, weighted for a refined text region approximation.
2. Continuous Text Adjustment: Cropped text regions undergo repeated inversion tailoring, improving text feature capture by minimizing background interference.
3. Meticulous Text Delineation: A final precision-focused 2-means clustering defines exact text versus background delineations, ensuring comprehensive coverage of textual content.

Following text identification, TextDestroyer disrupts the textual latent space representation by introducing random Gaussian noise, obliterating any recognizable text data. The subsequent diffusion-guided reconstruction employs $KV$ combination strategies, harnessing latent codes from the original image to ensure seamless integration into the unaltered background context.

Experimental Results and Implications

The research presents a meticulous quantitative and qualitative evaluation, comparing TextDestroyer against recognized benchmarks like EraseNet, DeepEraser, and CTRNet. While TextDestroyer's performance metrics reveal its pioneering nature, particularly in eliminating residual text traces, it does show limitations in PSNR and MSSIM relative to conventionally trained models. Qualitative analysis, however, showcases its prowess in realistic scene reconstruction without recognizing text remains.

Future developments should explore refinement in background fidelity, improvements in handling typographically complex text (like curved or small fonts), and reductions in computational demand to enhance practical viability. This paper underscores the untapped potential of leveraging pre-trained models in novel applications, suggesting prospective expansions within AI-driven privacy safeguards and content adaptability across multimedia platforms.

In summary, TextDestroyer stands as a critical inquiry into the capabilities of diffusion models' latent operations, paving the way for broader applications in automated text anonymization and digital privacy assurance. Further research may consider enhancing its scope through integration with more robust pre-trained architectures and expanding its technical application range.