Search Results (164)

This paper discusses an adaptive method of image steganography issues based on the application of a linear hash function over the GF (2) field to control the embedding process. The method uses staggered splitting of an image into 8 × 8-pixel blocks to provide blind steganography. Classification thresholds are defined as the percentiles of the distribution of gradients throughout the image, allowing for efficient load distribution between textured and smooth areas. Experiments on the BOSSBase, SIPI and Kaggle kits show that the method provides an actual capacity of up to 0.7 bpp at PSNR 47–50 dB and is resistant to statistical tests and RS analysis. At the same time, like other approaches based on modification of pixel differences, it remains vulnerable to modern stegoanalysis based on spatial rich models (SRMs). However, thanks to the modular structure of embedding control based on linear hash function, the proposed architecture allows direct integration with many modern adaptive strategies aimed at minimizing statistical anomalies. Full article

This paper aims to secure sensitive data generated by IoT devices by introducing a lightweight hybrid approach that combines steganography and cryptography. While classical cryptography offers confidentiality guarantees, the visibility of the produced ciphertexts keeps them at risk of traffic analysis, which could reveal communication patterns. Although some studies use Curve25519-based protocols, ECC paired with RDWT, or VLSB-based steganography, there is no complete approach that combines cryptographic and steganographic methods that is tailored to IoT devices. Our proposed scheme addresses this gap by integrating X25519 with Elligator 2 for efficient key exchange, using Ascon-AEAD128 for encryption, and finally hiding the encrypted payload within cover images using hybrid DWT-DCT steganography. When compared to similar hybrid approaches, our method achieves better performance, with results showing high imperceptibility, low computational overhead, and good resistance to noise. The cryptographic-steganographic combo adopted by our proposed framework improves confidentiality, integrity, and resistance to detection in resource-constrained IoT systems. Full article

We propose Distribution-Preserving Latent Steganography via Conditional Optimal Transport (DPL-COT), a coverless image steganography framework for latent diffusion models. Unlike classical cover-modifying schemes, DPL-COT embeds a bitstream directly into the initialization noise latent ${\mathbf{z}}_T\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ without model retraining. Our primary objective is high recoverability and a low bit error rate (BER) under deterministic inversion, which is inherently imperfect due to numerical discretization and VAE nonlinearity. To maximize decoding stability, we restrict embedding to the natural tails of the latent prior by selecting the largest-magnitude coordinates, thereby increasing the sign decision margin against inversion drift. To preserve distributional stealth, per-bit target values are analytically derived from truncated Gaussians matching the marginal distribution of the selected coordinates. Conditional 1D optimal transport is applied independently for each bit class, mapping every coordinate to its target value while preserving rank order. We generate 5000 stego images using a pretrained diffusion model and demonstrate a favorable capacity–reliability trade-off (e.g., 4916 bits/image with 0.473% mean BER) and strong robustness to JPEG compression (sub-1% mean BER at $Q=60$). Compared with LDStega, a recent LDM-based scheme reporting 99.28% clean-channel accuracy, DPL-COT achieves 99.53% at a comparable operating point and sustains above-99% accuracy under all tested JPEG quality factors. Latent-space tests further confirm negligible cover–stego distribution shift (mean ${\mathrm{KS}}_2<0.003$, mean $W_1<0.003$), a property not formally addressed by prior methods. Full article

In this paper, a novel hybrid edge-detection steganography technique is proposed, which greatly increases the payload capacity without losing much of its invisibility. Conventional least significant bit (LSB) steganography has a low payload capacity and is sensitive to statistical analysis. Our approach combines Canny and Sobel edge-detection methods to find the optimal embedding regions and then performs Most Significant Bit (MSB) modifications in edge areas where the human visual system (HVS) is less sensitive to changes. Experimental results show that the performance of our proposed method outperforms conventional LSB-based steganographic methods by an average of 42.3% in payload capacity, while maintaining a PSNR greater than 38 dB and an SSIM above 0.95. The proposed method is also more robust against statistical attacks, such as chi-square analysis and RS steganalysis, which are critical challenges in secure data transmission. Full article

Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset ($256\times 256$ pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a $0\%$ BER for secret information of $8\times 8$ to $64\times 64$ bits, and the BER is only $0.00002\%$ for $256\times 256$-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method ($64\times 64$ bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB ($256\times 256$), and the BER of SteganoGAN is reduced by $99.99\%$ ($256\times 256$ bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤$64\times 64$ bit) and supports up to $256\times 256$-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security. Full article

The purpose of digital image steganalysis is to identify the signal embedded in the natural image by steganography. In the spatial domain, this embedded signal only modifies the image value of the natural image by $\pm 1$, and this modification is weak. However, most of the existing convolutional neural networks use popular components to design or optimize the network structure, without deeply exploring the network’s ability to recognize such weak modifications. In order to deal with this problem, we propose a novel preprocessing structure, the learnable filter constrained by high-pass prior (LFCHP), to improve the network’s ability to extract weak embedded signals in the preprocessing stage, as well as a second-order signal auxiliary branch (SSAB) to reduce the suppression of weak embedded signals during convolution stacking, and a new pooling method, SoftPool, to reduce the loss of weak embedded signals during downsampling. Combining these three structures, we propose a steganalysis network, WSERNet, for weak steganographic signal extraction and enhancement. Experiments conducted under identical conditions demonstrate that the proposed method achieves an accuracy improvement of 1.08–2.96% over state-of-the-art spatial-domain steganalysis algorithms across three steganographic schemes at four embedding rates, and exhibits excellent generalization capabilities across different steganography techniques. Full article

Existing style-transfer steganography schemes suffer from three critical limitations: insufficient robustness against online social network (OSN) processing pipelines, susceptibility to steganalytic detection, and degraded visual quality. To address these challenges holistically, we propose StegTransfer—a unified framework that integrates: (1) forward non-differentiable distortion simulation, which emulates realistic OSN operations to enhance robustness; (2) adversarially hardened embedding through joint training with steganalyzers to improve security; and (3) payload-preserving style enhancement that optimizes visual aesthetics without sacrificing embedding capacity. Experimental evaluations demonstrate that StegTransfer achieves superior performance in visual fidelity (NIMA score: 6.32), robustness (PSNR up to 30.2 dB under JPEG compression), and security (detection rates as low as 15.5% and 62.3% under StegExpose and SiaStegNet, respectively. Full article

The rapid growth of digital communication has heightened the need for the secure transfer of sensitive image data. This is due to the increasing threats posed by cyberattacks and unauthorized access. Traditional encryption methods, while effective for text and binary data, often face significant challenges when applied to images, due to their larger size and complex structure. These characteristics make it difficult to provide a robust security solution. In this paper, we present a fast and efficient hybrid image encryption and steganography algorithm that leverages a substitution–permutation network (SPN), a chaotic logistic map (CLM), and least-significant-bit (LSB) substitution. This approach aims to improve data security and confidentiality while maintaining low computational complexity. The chaotic map generates random sequences for substitution and permutation, ensuring high unpredictability. The SPN framework improves the confusion and diffusion properties of the encryption process. The LSB substitution method hides the encrypted data values within the pixels of the cover image. We evaluate the security and efficiency of the proposed algorithm using various statistical tests, including measurement of the mean square error (MSE) and peak signal-to-noise ratio (PSNR) and pixel difference histogram (PDH) analysis. The results indicate that our algorithm outperforms many existing methods in terms of speed and efficiency, making it suitable for real-time hybrid encryption and steganography applications. Full article

The merging of the Internet of Things (IoT) and Artificial Intelligence (AI) advances has intensified challenges related to data authenticity and security. These advancements necessitate a multi-layered security approach to ensure the security, reliability, and integrity of critical infrastructure and intelligent surveillance systems. This paper proposes a two-layered security approach that combines a discrete cosine transform least significant bit 2 (DCT-LSB-2) with artificial neural networks (ANNs) for data forensic validation and mitigating deepfakes. The proposed model encodes validation codes within the LSBs of cover images captured by an IoT camera on the sender side, leveraging the DCT approach to enhance the resilience against steganalysis. On the receiver side, a reverse DCT-LSB-2 process decodes the embedded validation code, which is subjected to authenticity verification by a pre-trained ANN model. The ANN validates the integrity of the decoded code and ensures that only device-originated, untampered images are accepted. The proposed framework achieved an average SSIM of 0.9927 across the entire investigated embedding capacity, ranging from 0 to 1.988 bpp. DCT-LSB-2 showed a stable Peak Signal-to-Noise Ratio (average 42.44 dB) under various evaluated payloads ranging from 0 to 100 kB. The proposed model achieved a resilient and robust multi-layered data forensic validation system. Full article

Image steganography is crucial for secure communication, enabling covert data embedding within cover images. While traditional methods such as LSB embedding are vulnerable to detection, deep learning techniques like GANs and autoencoders have improved performance, yet they still struggle with dynamic adaptation to diverse secret data types, limited training datasets, and resilience to distortions. To address these issues, we propose a flexible framework with adaptive multi-encoder–decoder pairs, extensive dataset training, and an optimized architecture with specialized components. Our model achieves significant improvements in Secret Recovery Accuracy (SRA), Stego-Image Quality (SSIM, PSNR), and robustness to noise, with SSIM of 0.99 and recovery accuracy over 98%. It also reduces the detection rate, with an AUC approaching 0.5 in steganalysis. These results set a new benchmark for secure image transmission and privacy-preserving communication. Full article

With the increasing sophistication of Artificial Intelligence (AI), traditional digital steganography methods face a growing risk of being detected and compromised. Adversarial attacks, in particular, pose a significant threat to the security and robustness of hidden information. To address these challenges, this paper proposes a novel AI-based steganography framework designed to enhance the security of concealed messages within digital images. Our approach introduces a multi-stage embedding process that utilizes a sequence of encoder models, including a base encoder, a residual encoder, and a dense encoder, to create a more complex and secure hiding environment. To further improve robustness, we integrate Wavelet Transforms with various deep learning architectures, namely Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), and Graph Convolutional Networks (GCNs). We conducted a comprehensive set of experiments on the FashionMNIST and MNIST datasets to evaluate our framework’s performance against several adversarial attacks. The results demonstrate that our multi-stage approach significantly enhances resilience. Notably, while CNN architectures provide the highest baseline accuracy, BNNs exhibit superior intrinsic robustness against gradient-based attacks. For instance, under the Fast Gradient Sign Method (FGSM) attack on the MNIST dataset, our BNN-based models maintained an accuracy of over 98%, whereas the performance of comparable CNN models dropped sharply to between 10% and 18%. This research provides a robust and effective method for developing next-generation secure steganography systems. Full article

With the rapid proliferation of digital multimedia in resource-constrained Internet of Things (IoT) environments, there is growing demand for efficient image compression combined with secure data embedding. Existing Vector Quantization (VQ)-based Reversible Data Hiding (RDH) methods prioritize embedding capacity while neglecting reconstruction fidelity, often introducing noticeable quality degradation in edge regions—unacceptable for high-fidelity applications such as medical imaging and forensic analysis. This paper proposes a lightweight RDH framework with a once-offline trained VQ codebook that simultaneously performs secure data embedding and visual quality enhancement for VQ-compressed images. Quality Improvement Codes (QIC) are generated from pixel-wise residuals between original and VQ-decompressed images and embedded into the VQ index table using a novel Recoding Index Value (RIV) mechanism without additional transmission overhead. Sobel edge detection identifies perceptually sensitive blocks for targeted enhancement. Comprehensive experiments on ten standard test images across multiple resolutions (256 × 256, 512 × 512) and codebook sizes (64–1024) demonstrate Peak Signal-to-Noise Ratio (PSNR) gains of +4 to +5.39 dB and Structural Similarity Index Measure (SSIM) improvements of +4.12% to +9.86%, with embedding capacities approaching 100 Kbits. The proposed approach consistently outperforms existing methods in both image quality and payload capacity while eliminating computational overhead of deep learning models, making it highly suitable for resource-constrained edge devices and real-time multimedia security applications. Full article

Image steganography is often employed in information security and confidential communications, yet it typically faces challenges of imperceptibility and robustness during transmission. Meanwhile, insufficient attention has been paid to preserving the quality of the secret image after JPEG compression at the receiver, which limits the effectiveness of steganography. In this study, we propose an anti-compression attention-based diffusion pattern steganography model using GAN (ADPGAN). ADPGAN leverages dense connectivity to fuse shallow and deep image features with secret data, achieving high robustness against JPEG compression. Meanwhile, an enhanced attention module and a discriminator are employed to minimize image distortion caused by data embedding, thereby significantly improving the imperceptibility of the host image. Based on ADPGAN, we propose a novel JPEG-compression-resistant image framework that improves the quality of the recovered image by ensuring that the degradation of the reconstructed image primarily stems from sampling rather than JPEG compression. Unlike direct embedding of full-size secret images, we downsample the secret image into a secret data stream and embed it into the cover image via ADPGAN, demonstrating high distortion resistance and high-fidelity recovery of the secret image. Ablation studies validate the effectiveness of ADPGAN, achieving a 0-bit error rate (BER) under JPEG compression at a quality factor of 20, yielding an average Peak Signal-to-Noise Ratio (PSNR) of 39.70 dB for the recovered images. Full article

A steganography technique hides a secret message stealthily within multimedia files such as images, videos, or even the skin image of an avatar in a metaverse environment. Conversely, a steganalysis technique detects steganographic files containing hidden messages. Recently, with the rapid advancement of Convolutional Neural Network (CNN) architectures, CNN-based image steganalysis models have been proposed to accurately detect steganography in image files. Meanwhile, Deep Learning (DL) models, including CNNs, are known to be vulnerable to evasion attacks such as adversarial example attacks, which can cause a CNN-based classifier to misclassify an input image according to the attacker’s intent. Given the lack of prior research in this domain, this paper investigates how effectively state-of-the-art adversarial example attack methods can evade three representative CNN-based image steganalysis ML models (XuNet, YeNet, and SRNet). Specifically, we first describe a system model consisting of three participating entities—a naïve attacker, a defender (Defender Lv. 1 and Defender Lv. 2), and an adversarial attacker. Next, we present experimental results comparing nine adversarial example attack methods against the three representative CNN models in terms of various metrics, including classification accuracy (CA), missed detection rate (MDR), attack success index (ASI), and adversarial example generation time (AEGT). Full article

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