Search Results (93)

As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune to typical steganalysis methods and can better protect the secret message from being exposed. However, existing SWE methods are generally criticized for their poor payload capacity and low fidelity of recovered secret messages. In this paper, we propose a novel steganography-without-embedding technique, named DF-SWE, which addresses the aforementioned drawbacks and produces diverse and natural stego images. Specifically, DF-SWE employs a reversible circulation of double flow to build a reversible bijective transformation between the secret image and the generated stego image. Hence, it provides a way to directly generate stego images from secret images without a cover image. Besides leveraging the invertible property, DF-SWE can invert a secret image from a generated stego image in a nearly lossless manner and increase the fidelity of extracted secret images. To the best of our knowledge, DF-SWE is the first SWE method that can hide multiple images into one image with the same size, significantly enhancing the payload capacity. According to the experimental results, the payload capacity of DF-SWE achieves 24–72 BPP, which is 8000∼16,000 times more compared to its competitors while producing diverse images to minimize the exposure risk. Importantly, DF-SWE can be applied in the steganography of secret images in various domains without requiring training data from the corresponding domains. This domain-agnostic property suggests that DF-SWE can (1) be applied to hiding private data and (2) be deployed in resource-limited systems. Full article

Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in a hybrid, partially data-driven approach. The proposed scheme first leverages an adversarial neural network (UT-GAN, Universal Transform Generative Adversarial Network) to generate stego images as a preprocessing step. Subsequently, it dynamically adjusts the cost function with the aid of a DCTR (Discrete Cosine Transform Residual)-based gradient calculator to optimize the images, ensuring that the final adversarial images can resist detection by steganalysis tools. The encoder in this scheme adopts a unique architecture, where its internal parameters are determined by a partially data-driven mechanism. This design not only enhances the capability of traditional steganography schemes to counter advanced steganalysis technologies but also effectively reduces the computational overhead during stego image generation. Full article

Audio steganography exploits redundancies in the human auditory system to conceal secret information within cover audio, ensuring that the hidden data remains undetectable during normal listening. However, recent research shows that current audio steganography techniques are vulnerable to detection by deep learning-based steganalyzers, which analyze the high-dimensional features of stego audio for classification. While deep learning-based steganography has been extensively studied for image covers, its application to audio remains underexplored, particularly in achieving robust embedding and extraction with minimal perceptual distortion. We propose a diffusion-based audio steganography model comprising two primary modules: (i) a diffusion-based embedding module that autonomously integrates secret messages into cover audio while preserving high perceptual quality and (ii) a corresponding diffusion-based extraction module that accurately recovers the embedded data. The framework supports both pre-existing cover audio and the generation of high-quality steganographic cover audio with superior perceptual quality for message embedding. After training, the model achieves state-of-the-art performance in terms of embedding capacity and resistance to detection by deep learning steganalyzers. The experimental results demonstrate that our diffusion-based approach significantly outperforms existing methods across varying embedding rates, yielding stego audio with superior auditory quality and lower detectability. Full article

Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based diffusion embedding algorithm (CDEA) that uniquely leverages character-level statistics and a power-law-inspired grouping strategy to better balance candidate word selection. Unlike prior methods, the proposed CDEA explicitly prioritizes high-probability candidates, thereby improving both semantic consistency and text naturalness. When combined with XLNet, it effectively generates longer sensitive sequences while preserving quality. The experimental results showed that CDEA not only produces steganographic texts with higher imperceptibility and fluency but also achieves stronger resistance to steganalysis compared with the existing approaches. Future work will be to enhance statistical imperceptibility, integrate CDEA with larger language models such as GPT-5, and extend applications to cross-lingual, multimodal, and practical IoT or blockchain communication scenarios. Full article

Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention and bilinear codeword feature interaction mechanisms. First, this paper utilizes the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, the bilinear codeword feature interaction mechanism is used to capture an informative second-order codeword feature interaction pattern in a fine-grained way. Finally, multiple fully connected layers with a residual structure are utilized to capture higher-order codeword interaction features while preserving lower-order interaction features. The experimental results show that the performance of our method is better than that of the state-of-the-art frame-wise steganalysis method on large steganography datasets. The detection accuracy of our method is 74.46% on 1000K testing samples, whereas the detection accuracy of the state-of-the-art method is 72.32%. Full article

Linguistic steganography can be utilized to establish covert communication channels on social media platforms, thus facilitating the dissemination of illegal messages, seriously compromising cyberspace security. Synonym substitution-based linguistic steganography methods have garnered considerable attention due to their simplicity and strong imperceptibility. Existing linguistic steganalysis methods have not achieved excellent detection performance for the aforementioned type of linguistic steganography. In this paper, based on the idea of focusing on accumulated differences, we propose a two-stage synonym substitution-based linguistic steganalysis method that does not require a synonym database and can effectively detect texts with very low embedding rates. Experimental results demonstrate that this method achieves an average detection accuracy 2.4% higher than the comparative method. Full article

As adaptive multi-rate (AMR) speech applications become increasingly widespread, AMR-based steganography presents growing security risks. Conventional steganalysis methods often assume known embedding rates, limiting their practicality in real-world scenarios where embedding rates are unknown. To overcome this limitation, we introduce a novel framework that integrates a multi-scale transformer architecture with multi-task learning for joint classification and regression. The classification task effectively distinguishes between cover and stego samples, while the regression task enhances feature representation by predicting continuous embedding values, providing deeper insights into embedding behaviors. This joint optimization strategy improves model adaptability to diverse embedding conditions and captures the underlying relationships between discrete embedding classes and their continuous distributions. The experimental results demonstrate that our approach achieves higher accuracy and robustness than existing steganalysis methods across varying embedding rates. Full article

Traditional reversible data hiding (RDH) methods prioritize the exact recovery of the original cover image; however, this rigidity often hinders both capacity and design flexibility. This study introduces a partial reversible data hiding (PRDH) framework that departs from conventional standards by allowing reversibility relative to a generated cover image rather than the original. The proposed system leverages a dual-image structure and an enhanced HC(7,4) Hamming code to synthesize virtual pixels, enabling efficient and low-distortion syndrome-based encoding. Notably, it achieves embedding rates up to 1.5 bpp with PSNR values exceeding 48 dB. While the proposed method avoids auxiliary data, its reliability hinges on paired image availability, which is a consideration for real-world deployment. Demonstrated resilience to RS-based steganalysis suggests viability in sensitive domains such as embedding structured metadata in diagnostic medical imagery. Nonetheless, further evaluation across more diverse image types and attack scenarios is necessary in order to confirm its generalizability. Full article

Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effective steganalysis methods, particularly those designed to extract hidden information. This paper introduces an innovative image steganalysis method based on generative adaptive Gabor residual networks with density-peak guidance (SG-ResNet). SG-ResNet employs a dual-stream collaborative architecture to achieve precise detection and reconstruction of steganographic information. The classification subnet utilizes dual-frequency adaptive Gabor convolutional kernels to decouple high-frequency texture and low-frequency contour components in images. It combines a density peak clustering with three quantization and transformation-enhanced convolutional blocks to generate steganographic covariance matrices, enhancing the weak steganographic signals. The reconstruction subnet synchronously constructs multi-scale features, preserves steganographic spatial fingerprints with channel-separated residual spatial rich model and pixel reorganization operators, and achieves sub-pixel-level steganographic localization via iterative optimization mechanism of feedback residual modules. Experimental results obtained with datasets generated by several public steganography algorithms demonstrate that SG-ResNet achieves State-of-the-Art results in terms of detection accuracy, with 0.94, and with a PSNR of 29 between reconstructed and original secret images. Full article

Modern linguistic steganography faces the fundamental challenge of balancing embedding capacity with detection resistance, particularly against advanced AI-based steganalysis. This paper presents DeepStego, a novel steganographic system leveraging GPT-4-omni’s language modeling capabilities for secure information hiding in text. Our approach combines dynamic synonym generation with semantic-aware embedding to achieve superior detection resistance while maintaining text naturalness. Through comprehensive experimentation, DeepStego demonstrates significantly lower detection rates compared to existing methods across multiple state-of-the-art steganalysis techniques. DeepStego supports higher embedding capacities while maintaining strong detection resistance and semantic coherence. The system shows superior scalability compared to existing methods. Our evaluation demonstrates perfect message recovery accuracy and significant improvements in text quality preservation compared to competing approaches. These results establish DeepStego as a significant advancement in practical steganographic applications, particularly suitable for scenarios requiring secure covert communication with high embedding capacity. Full article

Image steganalysis methods aim at detecting whether there exist hidden messages in images. Deep learning (DL) models have been proposed to enhance steganography detection. These models occupy a large amount of memory and, for this reason, should be optimized when the scenario involves resource-limited devices and systems. This work addresses different deep learning model optimization strategies, namely model pruning, quantization and weight clustering, applied to a deep learning model that presents competitive accuracy results in image steganalysis and belongs to the family of DL models with smaller memory requirements. The results show that the use of optimization schemes can lead to similar or even better accuracy compared to the original model (without the use of optimization schemes), while requiring less memory to store the model. Different scenarios are simulated for each optimization technique, and, finally, quantization is combined with pruning. For dynamic range quantization (DRQ), we achieve models that can save approximately 72% of storage. For FP16 quantization, we obtain better accuracy results and a model with approximately 50% less memory consumption. By applying weight clustering, we also achieve compressed models that can save more than 72% of storage space and lead to better accuracy for some scenarios. Using the combination of pruning and quantization, smaller models in terms of memory requirements are obtained. Full article

Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value. Full article

Steganography has been widely used in the field of image privacy protection. However, with the advancement of steganalysis techniques, deep learning-based models are now capable of accurately detecting modifications in stego-images, posing a significant threat to traditional steganography. To address this, we propose AGASI, a GAN-based approach for strengthening adversarial image steganography. This method employs an encoder as the generator in conjunction with a discriminator to form a generative adversarial network (GAN), thereby enhancing the robustness of stego-images against steganalysis tools. Additionally, the GAN framework reduces the gap between the original secret image and the extracted image, while the decoder effectively extracts the secret image from the stego-image, achieving the goal of image privacy protection. Experimental results demonstrate that the AGASI method not only ensures high-quality secret images but also effectively reduces the accuracy of neural network classifiers, inducing misclassifications and significantly increasing the embedding capacity of the steganography system. For instance, under PGD attack, the adversarial stego-images generated by the GAN, at higher disturbance levels, successfully maintain the quality of the secret image while achieving an 84.73% misclassification rate in neural network detection. Compared to images with the same visual quality, our method increased the misclassification rate by 23.31%. Full article

Steganography serves a crucial role in secure communications by concealing information within non-suspicious media, yet traditional methods often lack resilience and efficiency. Distributed steganography, which involves fragmenting messages across multiple containers using secret sharing schemes, offers improved security but increases complexity. This paper introduces a novel two-phase embedding algorithm that mitigates these issues, enhancing both security and practicality. Initially, the secret message is divided into shares using Shamir’s Secret Sharing and embedded into distinct media containers via pseudo-random LSB paths determined by a unique internal stego key. Subsequently, this internal key is further divided and embedded using a shared stego key known only to the sender and receiver, adding an additional security layer. The algorithm effectively reduces key management complexity while enhancing resilience against sophisticated steganalytic attacks. Evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), demonstrate that stego images maintain high quality even when embedding up to 0.95 bits per pixel (bpp). Additionally, robustness tests with StegoExpose and Aletheia confirm the algorithm’s stealthiness, as no detections are made by these advanced steganalysis tools. This research offers a secure and efficient advancement in distributed steganography, facilitating resilient information concealment in sophisticated communication environments. Full article

To defeat AI-based steganalysis systems, various techniques using adversarial example attack methods have been reported. In these techniques, adversarial stego images are generated using adversarial attack algorithms and steganography embedding algorithms sequentially and independently. However, this approach can be inefficient because both algorithms independently insert perturbations into a cover image, and the steganography embedding algorithm could significantly lower the undetectability or indistinguishability of adversarial attacks. To address this issue, we propose an innovative adversarial stego image generation method (ASIGM) that fully integrates the two separate algorithms by using the Jacobian-based Saliency Map Attack (JSMA). JSMA, one of the representative l₀ norm-based adversarial example attack methods, is used to compute a set of pixels in the cover image that increases the probability of being classified as the non-stego class by the steganalysis model. The reason for this calculation is that if a secret message is inserted into the limited set of pixels in such a way, noise is only required for message embedding, and even misclassification of the target steganalysis model can be achieved without additional noise insertion. The experimental results demonstrate that our proposed ASIGM outperforms two representative steganography methods (WOW and ADS-WOW). Full article