Generative steganography, a novel paradigm in information hiding, has garnered considerable attention for its potential to withstand steganalysis. However, existing generative steganography approaches suffer from the limited visual quality of generated images and are challenging to apply to lossy transmissions in real-world scenarios with unknown channel attacks. To address these issues, this paper proposes a novel robust generative image steganography scheme, facilitating zero-shot text-driven stego image generation without the need for additional training or fine-tuning. Specifically, we employ the popular Stable Diffusion model as the backbone generative network to establish a covert transmission channel. Our proposed framework overcomes the challenges of numerical instability and perturbation sensitivity inherent in diffusion models. Adhering to Kerckhoff’s principle, we propose a novel mapping module based on dual keys to enhance robustness and security under lossy transmission conditions. Experimental results showcase the superior performance of our method in terms of extraction accuracy, robustness, security, and image quality.

The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society’s stability and security. Therefore, fake news detection has garnered extensive research interest in the field of social forensics. Current methods primarily concentrate on the integration of textual and visual features but fail to effectively exploit multi-modal information at both fine-grained and coarse-grained levels. Furthermore, they suffer from an ambiguity problem due to a lack of correlation between modalities or a contradiction between the decisions made by each modality. To overcome these challenges, we present a Multi-grained Multi-modal Fusion Network (MMFN) for fake news detection. Inspired by the multi-grained process of human assessment of news authenticity, we respectively employ two Transformer-based pre-trained models to encode token-level features from text and images. The multi-modal module fuses fine-grained features, taking into account coarse-grained features encoded by the CLIP encoder. To address the ambiguity problem, we design uni-modal branches with similarity-based weighting to adaptively adjust the use of multi-modal features. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods on three prevalent datasets.

While the existing reserving room before encryption (RRBE) based reversible data hiding in encrypted image (RDHEI) schemes can achieve decent embedding capacity, the capacity of the existing vacating room by encryption (VRBE) based schemes is relatively low. To address this issue, this paper proposes a generalized framework for high-capacity RDHEI for both the RRBE and VRBE cases. First, an efficient embedding room generation algorithm (ERGA) is designed to produce large embedding room using pixel prediction and entropy encoding. Then, we propose two RDHEI schemes, one for RRBE, another for VRBE. In the RRBE scenario, the image owner generates the embedding room with ERGA and encrypts the preprocessed image using stream cipher with two encryption keys. Then, the data hider locates the embedding room and embeds the additional encrypted data. In the VRBE scenario, the cover image is encrypted by an improved block modulation and permutation encryption algorithm, where the spatial redundancy in the plain-text image is greatly preserved. Then, the data hider applies ERGA on the encrypted image to generate the embedding room and conducts data embedding. For both schemes, receivers with different authentication keys can conduct either error-free data extraction or error-free image recovery. The experimental results show that the two proposed schemes outperform many state-of-the-art RDHEI schemes. Besides, they can ensure high security level, where the original image can be hardly discovered from the encrypted version before or after data hiding by unauthorized users.

Traditional reversible data hiding (RDH) focuses on enlarging the embedding payloads while minimizing the distortion with a criterion of mean square error (MSE). Since imperceptibility can also be achieved via image processing, we propose a novel method of RDH with contrast enhancement (RDH-CE) using histogram shifting. Instead of minimizing the MSE, the proposed method generates marked images with good quality with the sense of structural similarity. The proposed method contains two parts: the baseline embedding and the extensive embedding. In the baseline part, we first merge the least significant bins to reserve spare bins and then embed additional data by a histogram shifting approach using arithmetic encoding. During histogram shifting, we propose to construct the transfer matrix by maximizing the entropy of the histogram. After embedding, the marked image containing additional data has a larger contrast than the original image. In the extensive embedding part, we further propose to concatenate the baseline embedding with an MSE-based embedding. On the recipient side, the additional data can be extracted exactly, and the original image can be recovered losslessly. Comparing with existing RDH-CE approaches, the proposed method can achieve a better embedding payload.

Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered. This paper presents Imuge+, an enhanced scheme for image immunization. By observing the invertible relationship between image immunization and the corresponding self-recovery, we employ an invertible neural network to jointly learn image immunization and recovery respectively in the forward and backward pass. We also introduce an efficient attack layer that involves both malicious tamper and benign image post-processing, where a novel distillation-based JPEG simulator is proposed for improved JPEG robustness. Our method achieves promising results in real-world tests where experiments show accurate tamper localization as well as high-fidelity content recovery. Additionally, we show superior performance on tamper localization compared to state-of-the-art schemes based on passive forensics.