With the emergence of adversarial steganography, existing specialized steganalysis models suffer a significant performance decline in detecting non-homologous adversarial steganographic methods (i.e., trained on traditional-based method and tested on adversarial-based method), resulting in insufficient robustness in complex network environments. To address this issue, we propose a two-stage robust steganalysis framework with multi-feature enhancement against adversarial steganography. The framework integrates edge-aware attention with multi-dimensional statistical features to enhance robustness against adversarial steganography. In the first stage, we design a covariance pooling based convolutional neural network and integrate an edge-aware attention mechanism to improve the feature representation of subtle steganographic traces, enabling fast detection for most samples. In the second stage, samples with uncertain confidence scores from the first stage are further analyzed by extracting block-wise entropy features, global entropy features, and SRM co-occurrence features, followed by dimensionality reduction via principal component analysis (PCA) and classification using a random forest. The final decision is made through the collaborative fusion of the two stages. Experimental results demonstrate that the proposed method achieves excellent detection performance (2.57% average improvement over the existing best method) with strong robustness for adversarial steganography, and its generalization capability is further validated in cross-dataset scenarios. Furthermore, comprehensive ablation studies validate the efficacy of the network architecture.

Most existing steganographic techniques are primarily designed for grayscale images.When directly applied to color images without considering inter-channel color interactions, their security can be significantly compromised.In this paper, we propose a novel color image steganography method based on generative adversarial networks (GANs).Our framework features a dual-branch generator and a discriminator equipped with multiple steganalytic networks, each focused on a specific color channel.This architecture enables the progressive learning of asymmetric embedding costs across channels from scratch.We also introduce a phased training strategy that facilitates the learning of inter-channel color interactions and optimizes the security of each color channel in distinct phases, improving overall security.Furthermore, we propose a new adaptive update strategy and introduce the Mean Absolute Deviation (MAD) loss function to maintain a dynamic balance between the generator and discriminator, thereby progressively enhancing the generator's steganographic performance during training.Extensive comparative experiments demonstrate that the proposed method achieves state-of-the-art security against color image steganalysis.Comprehensive ablation studies further validate the effectiveness and rationale behind our approach.

Traditional steganographic techniques have often relied on manually crafted attributes related to image residuals. These methods demand a significant level of expertise and face challenges in integrating diverse image residual characteristics. In this paper, we introduce an innovative deep learning-based methodology that seamlessly integrates image residuals, residual distances, and image local variance to autonomously learn embedding probabilities. Our framework includes an embedding probability generator and three pivotal guiding components: Residual guidance strives to facilitate embedding in complex-textured areas. Residual distance guidance aims to minimize the residual differences between cover and stego images. Local variance guidance effectively safeguards against modifications in regions characterized by uncomplicated or uniform textures. The three components collectively guide the learning process, enhancing the security performance. Comprehensive experimental findings underscore the superiority of our approach when compared to traditional steganographic methods and randomly initialized ReLOAD in the spatial domain.

Current deep learning-based steganalyzers often depend on specific image dimensions, leading to inevitable adjustments in network structure when dealing with varied image sizes. This impedes their effectiveness in managing the wide range of image sizes commonly found on social media. To address this issue, our paper presents a novel steganalytic network that is optimized for fixed-size (notably,$256\times 256$) color images, but is capable of efficiently detecting stego images of arbitrary size without needing retraining or modifications to the network. Our proposed network is comprised of four modules. In the initial stem module, we calculate truncated residuals for each color channel of the input image. Diverging from existing steganalytic networks that rely on vanilla convolution, we have developed a pixel difference convolution module designed to better capture the artifacts introduced by steganography. Following this, we introduce an enhanced Transformer module with selective pooling, aimed at more effectively extracting global steganalytic features. To guarantee our network’s adaptability to different image sizes, we have developed a selective pooling strategy. This involves using global covariance pooling for fixed-size color images and spatial pyramid pooling for color images of various other sizes. This approach effectively standardizes the feature maps into uniform feature vectors. The final module is focused on classification. Extensive testing results on the ALASKA II color image dataset have demonstrated that our approach significantly improves detection performance for both fixed-size and arbitrary-size images, achieving state-of-the-art results. Additionally, we provide numerous ablation studies to confirm the effectiveness and soundness of our proposed network architecture.

Up to now, most existing steganalytic methods were designed for grayscale images, and are not suitable for the color images that are widely used in social networks. In this paper, we design a universal color image steganalysis network (called UCNet) for the spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganalytic features in each color channel, the preprocessing module first separates the input image into three channels based on the corresponding embedding spaces (i.e., RGB in the spatial domain, and YCbCr in the JPEG domain), and then extracts the image residuals with 62 fixed high-pass filters. Finally, all truncated residuals are concatenated for subsequent analysis, rather than adding them together in the first layer as in existing CNN-based steganalyzers. To accelerate network convergence and effectively reduce the number of parameters, the convolutional module contains three carefully designed types of layers with different shortcut connections and group convolution structures, to further learn the high-level steganalytic features. In the classification module, we employ global average pooling and a fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results that are comparable with other modern CNN-based steganalyzers (e.g., SRNet and LC-Net) in both the spatial and JPEG domains, with relatively few memory requirements and short training times. Furthermore, we also provide some necessary descriptions and carry out numerous ablation experiments to verify the rationality of the network design.

Perceptual encryption has received widespread attention as a technology for protecting multimedia visual information. In multimedia applications, perceptual encryption only protects a portion of the content without previewing all the information. At present, perceptual encryption is mostly oriented to conventional plain images while few methods aim at visual security measures for perceptually encrypted images. Existing solutions usually adopt well-known quality assessment metrics to measure the visual quality of encrypted images. However, they often exhibit undesired behavior on perceptually encrypted images with low quality. As a typical representation of three-dimensional scenes, light field images record the intensity and direction of light during propagation which is distinct from 2-D images. In this paper, we construct a perceptually encrypted light field image database (PE-LFID) for quality assessment based on 14 reference light field plaintext images. For each scene, we employ four encryption methods, each of which has six levels. Additionally, a novel visual security evaluation method based on PE-LFID is proposed by taking into account the local and global features of the light field images. First, we use a multi-threshold edge detection method to obtain the edge similarity in the spatial domain of the light field image. Afterwards, the epipolar plane image (EPI) generated from the angular domain of the light field image is used to calculate the gradient magnitude similarity, which is expressed as a global feature. Furthermore, the final quality prediction score is calculated by adaptively weighting between local and global features. We conduct extensive experiments on the proposed PE-LFID to assess the performance of classical and state-of-the-art IQA models. The experimental results demonstrate the effectiveness of the proposed method for visual security evaluation of perceptually encrypted light field images, as well as the scalability of PE-LFID.