Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size

• URL: http://arxiv.org/abs/2509.24823v1
• Date: Mon, 29 Sep 2025 14:10:15 GMT
• Title: Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size
• Abstract summary: We propose a high-payload image watermarking method for textual embedding.<n>A semantic description of the image - which may also correspond to the input text prompt- is embedded inside the image.<n> Experiments show that the proposed method is extremely robust against a wide variety of image processing.
• Score: 17.310703869759976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a high-payload image watermarking method for textual embedding, where a semantic description of the image - which may also correspond to the input text prompt-, is embedded inside the image. In order to be able to robustly embed high payloads in large-scale images - such as those produced by modern AI generators - the proposed approach builds upon a traditional watermarking scheme that exploits orthogonal and turbo codes for improved robustness, and integrates frequency-domain embedding and perceptual masking techniques to enhance watermark imperceptibility. Experiments show that the proposed method is extremely robust against a wide variety of image processing, and the embedded text can be retrieved also after traditional and AI inpainting, permitting to unveil the semantic modification the image has undergone via image-text mismatch analysis.

Related papers

• EDITOR: Effective and Interpretable Prompt Inversion for Text-to-Image Diffusion Models [31.31018600797305]
  We propose a prompt inversion technique called sys for text-to-image diffusion models.<n>Our method outperforms existing methods in terms of image similarity, textual alignment, prompt interpretability and generalizability.
  arXiv  Detail & Related papers  (2025-06-03T16:44:15Z)
• SWIFT: Semantic Watermarking for Image Forgery Thwarting [20.585348798260362]
  We modify the HiDDeN deep-learning watermarking architecture to embed and extract high-dimensional real vectors representing image captions.<n>Our method improves significantly on both malign and benign edits.
  arXiv  Detail & Related papers  (2024-07-26T09:50:13Z)
• Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking [20.320229647850017]
  Stable diffusion (SD) models have typically flourished in the field of image synthesis and personalized editing. The exposure of AI-created content on public platforms could raise both legal and ethical risks. In this work, we propose a Safe and high-traceable Stable Diffusion framework (namely SafeSD) to adaptive implant the watermarks into the imperceptible structure.
  arXiv  Detail & Related papers  (2024-07-18T05:53:17Z)
• Deep Learning-based Text-in-Image Watermarking [4.938567115890841]
  We introduce a novel deep learning-based approach to text-in-image watermarking. Our method embeds and extracts textual information within images to enhance data security and integrity.
  arXiv  Detail & Related papers  (2024-04-19T18:52:07Z)
• MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask [84.84034179136458]
  A crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning. We propose an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token to the image features. Our method, termed MaskDiffusion, is training-free and hot-pluggable for popular pre-trained diffusion models.
  arXiv  Detail & Related papers  (2023-09-08T15:53:37Z)
• T2IW: Joint Text to Image & Watermark Generation [74.20148555503127]
  We introduce a novel task for the joint generation of text to image and watermark (T2IW) This T2IW scheme ensures minimal damage to image quality when generating a compound image by forcing the semantic feature and the watermark signal to be compatible in pixels. We demonstrate remarkable achievements in image quality, watermark invisibility, and watermark robustness, supported by our proposed set of evaluation metrics.
  arXiv  Detail & Related papers  (2023-09-07T16:12:06Z)
• WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning [68.00975867932331]
  Existing watermark removal methods mainly rely on UNet with task-specific decoder branches. We introduce an implicit joint learning paradigm to holistically integrate information from both branches. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-08-20T07:56:34Z)
• Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation [63.061871048769596]
  We present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences. To be more specific, both input texts and images are encoded into one unified multi-modal latent space. Our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
  arXiv  Detail & Related papers  (2023-03-16T13:50:20Z)
• MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining [68.05105411320842]
  We propose a novel approach MaskOCR to unify vision and language pre-training in the classical encoder-decoder recognition framework. We adopt the masked image modeling approach to pre-train the feature encoder using a large set of unlabeled real text images. We transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder.
  arXiv  Detail & Related papers  (2022-06-01T08:27:19Z)
• Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors [58.71128866226768]
  Recent text-to-image generation methods have incrementally improved the generated image fidelity and text relevancy. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene. Our model achieves state-of-the-art FID and human evaluation results, unlocking the ability to generate high fidelity images in a resolution of 512x512 pixels.
  arXiv  Detail & Related papers  (2022-03-24T15:44:50Z)
• Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
  We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
  arXiv  Detail & Related papers  (2021-12-17T15:52:46Z)
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis [80.54273334640285]
  We propose a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators. We also propose a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images.
  arXiv  Detail & Related papers  (2020-08-13T12:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.