Flexible Cross-Modal Steganography via Implicit Representations

• URL: http://arxiv.org/abs/2312.05496v2
• Date: Tue, 12 Dec 2023 06:23:53 GMT
• Title: Flexible Cross-Modal Steganography via Implicit Representations
• Authors: Seoyun Yang, Sojeong Song, Chang D. Yoo, Junmo Kim
• Abstract summary: Our framework is considered for effectively hiding multiple data without altering the original INR ensuring high-quality stego data. Our framework can perform cross-modal steganography for various modalities including image, audio, video, and 3D shapes.
• Score: 41.777197453697056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present INRSteg, an innovative lossless steganography framework based on a novel data form Implicit Neural Representations (INR) that is modal-agnostic. Our framework is considered for effectively hiding multiple data without altering the original INR ensuring high-quality stego data. The neural representations of secret data are first concatenated to have independent paths that do not overlap, then weight freezing techniques are applied to the diagonal blocks of the weight matrices for the concatenated network to preserve the weights of secret data while additional free weights in the off-diagonal blocks of weight matrices are fitted to the cover data. Our framework can perform unexplored cross-modal steganography for various modalities including image, audio, video, and 3D shapes, and it achieves state-of-the-art performance compared to previous intra-modal steganographic methods.

Related papers

• TMI-CLNet: Triple-Modal Interaction Network for Chronic Liver Disease Prognosis From Imaging, Clinical, and Radiomic Data Fusion [3.2805467531625556]
  Chronic liver disease represents a significant health challenge worldwide. Recent evidence suggests that integrating multimodal data can provide more comprehensive prognostic information. We present the Triple-Modal Interaction Chronic Liver Network (TMI-CLNet)
  arXiv  Detail & Related papers  (2025-02-02T07:05:28Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored. We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Natias: Neuron Attribution based Transferable Image Adversarial Steganography [62.906821876314275]
  adversarial steganography has garnered considerable attention due to its ability to effectively deceive deep-learning-based steganalysis. We propose a novel adversarial steganographic scheme named Natias. Our proposed method can be seamlessly integrated with existing adversarial steganography frameworks.
  arXiv  Detail & Related papers  (2024-09-08T04:09:51Z)
• Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning [6.44069573245889]
  Brain tumor segmentation remains a significant challenge, particularly in the context of multi-modal magnetic resonance imaging (MRI) We propose a novel strategy, which is called masked predicted pre-training, enabling robust feature learning from incomplete modality data. In the fine-tuning phase, we utilize a knowledge distillation technique to align features between complete and missing modality data, simultaneously enhancing model robustness.
  arXiv  Detail & Related papers  (2024-06-12T20:35:16Z)
• FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival [3.4686401890974197]
  We propose a new end-to-end framework, FORESEE, for robustly predicting patient survival by mining multimodal information. Cross-fusion transformer effectively utilizes features at the cellular level, tissue level, and tumor heterogeneity level to correlate prognosis. The hybrid attention encoder (HAE) uses the denoising contextual attention module to obtain the contextual relationship features. We also propose an asymmetrically masked triplet masked autoencoder to reconstruct lost information within modalities.
  arXiv  Detail & Related papers  (2024-05-13T12:39:08Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Deep Cross-Modal Steganography Using Neural Representations [24.16485513152904]
  We propose a cross-modal steganography framework using Implicit Neural Representations (INRs) to hide secret data in cover images. The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions.
  arXiv  Detail & Related papers  (2023-07-02T08:08:02Z)
• CLIP-Driven Fine-grained Text-Image Person Re-identification [50.94827165464813]
  TIReID aims to retrieve the image corresponding to the given text query from a pool of candidate images. We propose a CLIP-driven Fine-grained information excavation framework (CFine) to fully utilize the powerful knowledge of CLIP for TIReID.
  arXiv  Detail & Related papers  (2022-10-19T03:43:12Z)
• Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion [71.87627318863612]
  We propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset.
  arXiv  Detail & Related papers  (2020-02-22T14:32:04Z)

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.