The rapid growth of generative models has led to a new direction in steganography called generative steganography (GS). It allows message-to-image generation without the need for a carrier image. Recently, generative steganography methods have been proposed using generative adversarial networks (GANs) and Flow models. On the one hand, methods that use GANs to generate stego images struggle to fully recover the hidden message because the networks are not reversible. On the other hand, methods based on Flow encounter a problem where the images they create might not look real, mainly because the network has limitations in being reversible. Diffusion models fulfill network reversibility while generating high-quality images. However, the framework of existing diffusion models is reversible, but hidden message recovery is not perfectly reversible, resulting in the recovered message being similar but not exactly the same as the hidden one. Existing diffusion models are typically trained for one-directional image generation tasks, so they face some problems when dealing with bi-directional steganography tasks. If pre-trained diffusion models are directly used to generate stego images, exact secret data extraction through the diffusion process cannot be achieved. In this paper, we present an improved generative steganography based on the diffusion model (GSD), which conceals secret data in the frequency domain of random noise to enhance the security and accuracy of steganography, and re-trains the denoising diffusion implicit model (DDIM) for steganography, called the StegoDiffusion. During training StegoDiffusion, random noise is injected into the clean natural images and then trained through the forward diffusion process to obtain the re-trained StegoDiffusion. Our proposed GSD scheme achieves a 100% extraction accuracy for hidden secret data with a payload of 1 bit-per-pixel (bpp) in a single channel, and generates high-quality stego images in PNG format.

Generative steganography (GS) is a novel data-hiding technique that generates stego images directly from secret data without using cover images, which is different from traditional steganography. However, existing steganography methods have shortcomings in terms of hiding capacity, extraction accuracy, and diversity of stego images. To address these limitations, we propose a high-performance Conditional Flow-based Generative Steganography (CFGS). First, to achieve exact extraction of secret data in high-capacity scenarios, we hide secret data in the frequency domain to resist the impact of stego image distortion. In addition, to enhance the diversity of stego images, we introduce a novel conditional generative flow model (C-Flow) to generate stego images, which consists of two newly designed layers, the Conditional Attention-based Affine Coupling layer and the Conditional Invertible Norm layer. C-Flow can accurately guide the visual content of stego images through different conditions, enhancing the diversity of stego images. Our approach is the first GS method capable of conditional guidance of stego image visual content, and achieves extraction accuracy of hidden secret data equal to or close to 100% for payloads up to 1 bit-per-pixel (bpp). Extensive experiments demonstrate that our proposed approach outperforms state-of-the-art GS methods.

With the development of robust information hiding (RIH) approaches, secret messages can be extracted successfully from stego-data after transmission through lossy channels of online social networks (OSNs). To interrupt illegal covert communications in OSNs, some methods sanitize the uploaded images by image processing operations to destroy the hidden data that may exist. However, none of the existing methods takes the RIH methods that can resist scaling into consideration, while scaling is a common operation in OSNs. In this paper, we first propose a general framework for image sanitization in OSN platforms, which serves as a countermeasure against the RIH. By using such a framework, the secret messages embedded in the upload images can be removed and the quality of the sanitized image can be well maintained. Our framework contains two deep neural networks: Scaling-Net and SC-Net. The Scaling-Net is dedicated to the sanitization of oversized images while the SC-Net is designed for other images. To achieve a good image quality, we also propose a discriminator for adversarial training of the Scaling-Net and SC-Net. Experimental results on different datasets demonstrate that our proposed method outperforms the state-of-the-art methods. The source code and pretrained models are available at our code repository (https://github.com/zyzhu19/Image_Sanitization).

Recent research has shown that deep learning based methods offer more accurate detection for image steganalysis than the traditional detection paradigm based on rich media models. Existing network architectures based on deep learning, however, stack more and more convolutional layers to increase local receptive fields for image stegananlysis. Limited by hardware, the detector with several convolutional layers may not extract features of steganography images from a global perspective effectively. In this paper, we propose a Convolutional Vision Transformer for image stegananlysis, which can capture both local and global dependencies among noise features. In image processing phase, our network preserves CNN frame for its capacity of producing image noise residuals. Different from previous methods, we utilize the attention mechanism of vision transformer for feature extraction and classification. The proposed network is validated on two public image datasets (BOSSbase 1.01 and ALASKA #2). Experimental results demonstrate that our network performs well over fixed-size dataset and arbitrary-size dataset.

A large number of multi-party conversation scenarios exist in social networks, which have been seldom studied in the field of human-machine conversation. In this paper, we study a novel task of joint learning for addressee selection and response generation in multi-party conversations. Systems are expected to select whom they address and generate the corresponding response. To solve it, we propose an end-to-end addressee selection and response generation (ASRG) model, containing an addressee selection module and a response generation module. In the selection module, we develop an addressee prediction attention scheme to obtain a unique context vector for each candidate, thereby calculating the probability of the candidate more accurately. In the generation module, we propose a Focus Transformer to generate responses. These two modules are jointly learnt to fully explore the correlations between addressee and response. Experimental results show ASRG remarkably outperforms baselines and generates relevant content for different addressees.

Generative steganography (GS) is a new data hiding manner, featuring direct generation of stego media from secret data. Existing GS methods are generally criticized for their poor performances. In this paper, we propose a novel flow based GS approach - Generative Steganographic Flow (GSF), which provides direct generation of stego images without cover image. We take the stego image generation and secret data recovery process as an invertible transformation, and build a reversible bijective mapping between input secret data and generated stego images. In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images. By reversing the mapping, hidden data can be extracted exactly from generated stego images. Furthermore, we propose a novel latent optimization strategy to improve the fidelity of stego images. Experimental results show our proposed GSF has far better performances than SOTA works.

Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this paper, we propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images. We firstly introduce the mutual information mechanism in GS, which helps to achieve high secret extraction accuracy. Our model contains four sub-networks, i.e., an image generator (G), a discriminator (D), a steganalyzer (S), and a data extractor (E). D and S act as two adversarial discriminators to ensure the visual quality and security of generated stego images. E is to extract the hidden secret from generated stego images. The generator G is flexibly constructed to synthesize either cover or stego images with different inputs. It facilitates covert communication by concealing the function of generating stego images in a normal generator. A module named secret block is designed to hide secret data in the feature maps during image generation, with which high hiding capacity and image fidelity are achieved. In addition, a novel hierarchical gradient decay (HGD) skill is developed to resist steganalysis detection. Experiments demonstrate the superiority of our work over existing methods.

Some robust data hiding approaches have been proposed to transmit secret data through online social networks (OSNs). Traditional steganalysis tools are inefficient in detecting these tailored steganographic methods. Although some algorithms have been developed to remove the hidden data of images, they are criticized for their low secret removal rate and poor image quality. Moreover, most of them are nongeneric methods, and multiple models must be trained to fit different algorithms. In this letter, we propose a general end-to-end data hiding break approach for OSNs, called the secret data remover (SDR). It is universal for algorithms of both robust steganography and robust watermarking, with which stego images are directly input and clean ones with the same appearances will then be generated. Moreover, we develop two novel techniques, namely, image fusion and latent renewal, to enhance the image quality and improve the overall performance. Experiments show that our proposed method achieves superior performance compared to state-of-the-art works. Hidden secret data are cleared while image quality is maintained or even slightly improved. At the same time, our work can be easily deployed in OSNs.