With the enhancement of people's security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review. 

A review of coverless steganography

DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network Scenarios

In addition to the advancements in deepfake generation, corresponding detection technologies need to continuously evolve to regulate the potential misuse of deepfakes, such as for privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing the current state of the art in this rapidly evolving field. We first unify task definitions, comprehensively introduce datasets and metrics, and discuss the development of generation and detection technology frameworks. Then, we discuss the development of several related sub-fields and focus on researching four mainstream deepfake fields: popular face swap, face reenactment, talking face generation, and facial attribute editing, as well as foreign detection. Subsequently, we comprehensively benchmark representative methods on popular datasets for each field, fully evaluating the latest and influential works published in top conferences/journals. Finally, we analyze the challenges and future research directions of the discussed fields. We closely follow the latest developments in https://github.com/flyingby/Awesome-Deepfake-Generation-and-Detection.

Deepfake Generation and Detection: A Benchmark and Survey

The progression of text-to-image generation models has incited an upsurge in disseminating generated images across social networks, providing a fertile ground for steganography. Presently, the majority of generated images are crafted utilizing black-box APIs and social networks employ lossy compression on uploaded images. However, there is a dearth of steganographic research conducted on black-box generated images, and the distinctive attributes of the generation model have not been harnessed, resulting in a performance that fails to achieve both security and robustness simultaneously. To address these challenges, we propose an innovative steganographic framework, Steganography based on Concomitantly shaRing generated Images and PrompTs (SCRIPT). This framework ensures security and robustness by precisely identifying robust coefficients within the image for message embedding and synchronizing their positions. For precise identification, we assess the ability of coefficients to withstand unknown spatial perturbations, employing this metric to quantify their robustness. For positional synchronization of robust coefficients, the relevant prompts are uploaded alongside the stego image, allowing the recipient to reconstruct the cover image using a mutually agreed random seed and the provided prompt. Subsequently, positional synchronization is achieved by consistently adopting an identical method for selecting robust coefficients between the sender and the recipient. By amalgamating these strategies, SCRIPT significantly surpasses prior algorithms. Empirical results validate our approach, with a noteworthy 98% message extraction success rate and a substantial 20%+ enhancement in security across diverse payloads.

Toward Secure and Robust Steganography for Black-Box Generated Images

Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc . With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g ., localization, segmentation, and alpha-matting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc . Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.

MFI-Net: Multi-feature Fusion Identification Networks for Artificial Intelligence Manipulation

Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.

/pdf/wgan-based-image-denoising-algorithm-d7crswp6.pdf

WGAN-Based Image Denoising Algorithm

Image steganographic methods based on encoder-decoder model with end-to-end network architecture recently have been proposed. However, in steganographic applications, the feature map (called stego matrix) generated by the encoder needs to be rounded as a real stego image for the receiver. The loss of precision by rounding stego matrix leads to the decline in the accuracy of extracted secret messages. The challenge of using end-to-end network to preserve robustness against rounding operation is that it is non-differentiable. In this paper, we propose an anti-rounding image steganography method with separable fine-tuning network architecture which includes the joint training stage (JT-stage) and the separable fine-tuning stage (SF-stage). Firstly, in JT-stage, an embedded generator and a stego matrix extractor are jointly learned without rounding operation. Utilizing concatenation in embedded generator can realistically fuse cover image and secret messages. And the multi-scale fusion block and residual dense block in stego matrix extractor can make secret messages more correctly decoded. Moreover, the discriminator is constructed by generative adversarial nets (GAN) in JT-stage to effectively improve the authenticity and steganalysis security. Then, in SF-stage, the embedded generator is frozen, and the stego matrix is obtained and rounded as a stego image. A stego image extractor is constructed by fine-tuning the layers of the stego matrix extractor to improve the accuracy of message extraction. As the loss will not backpropagate in the embedded generator, the non-differentiability of rounding operation can be offset. Experiments show that the proposed separation fine-tuning network is robust to rounding operation, and effectively reduces the degradation of the image quality and steganalysis performance. 

Anti-rounding Image Steganography with Separable Fine-tuned Network

Reversible data hiding in encrypted domain by signal reconstruction

Efficient and Secure Outsourced Image Watermarking in Cloud Computing

Efficient Fragile Privacy-Preserving Audio Watermarking Using Homomorphic Encryption

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