Search Results (70)

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

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

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

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

Modern traffic-monitoring systems increasingly rely on supplemental analytical data to complement video recordings, yet such data are rarely integrated into video containers without altering the original footage. This paper proposes a lightweight audio-based approach for embedding road-condition information using a Least Significant Bit (LSB) steganography framework. The method operates by serializing sensor data, encoding it into the LSB positions of synthetically generated audio, and subsequently compressing the audio track while preserving imperceptibility and video integrity. A series of controlled experiments evaluates how waveform type, sampling rate, amplitude, and frequency influence the storage efficiency and quality of WAV and FLAC stego-audio files. Additional tests examine the impact of embedding capacity and output-quality settings on compression behavior. Results reveal clear trade-offs between audio quality, data capacity, and file size, demonstrating that the proposed framework enables efficient, secure, and scalable integration of metadata into surveillance recordings. The findings establish practical guidelines for deploying LSB-based audio embedding in real traffic-monitoring environments. 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

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels. Full article

In a botnet attack, significant damage can occur when an attacker gains control over a large number of compromised network devices. Botnets have evolved from traditional centralized architectures to decentralized Peer-to-Peer (P2P) and hybrid forms. Recently, a steganography-based botnet (Stego-botnet) has emerged, which conceals command and control (C&C) messages within cover media such as images or video files shared over social networking sites (SNS). This type of Stego-botnet can evade conventional detection systems, as identifying hidden messages embedded in media transmitted via SNS platforms is inherently challenging. However, the inherent file size limitations of SNS platforms restrict the achievable payload capacity of such Stego-botnets. Moreover, the centralized characteristics of conventional botnet architectures expose attackers to a higher risk of identification. To overcome these challenges, researchers have explored network steganography techniques leveraging P2P networks such as BitTorrent, Google Suggest, and Skype. Among these, a hidden communication method utilizing Bitfield messages in BitTorrent has been proposed, demonstrating improved concealment compared to prior studies. Nevertheless, existing approaches still fail to achieve sufficient payload capacity relative to traditional digital steganography techniques. In this study, we extend P2P-based network steganography methods—particularly within the BitTorrent protocol—to address these limitations. We propose a novel botnet C&C communication model that employs network steganography over BitTorrent and validate its feasibility through experimental implementation. Furthermore, our results show that the proposed Stego-botnet achieves a higher payload capacity and outperforms existing Stego-botnet models in terms of both efficiency and concealment performance. Full article

Generative steganography (GS) generates stego-media via secret messages, but existing GS only targets single-type multimedia data with poor universality. The generator and extractor sizes are highly coupled with resolution. Message mapping converts secret messages and noise, yet current GS schemes based on it use gridded data, failing to generate diverse multimedia universally. Inspired by implicit neural representation (INR), we propose generative implicit steganography via message mapping (GIS). We designed single-bit and multi-bit message mapping schemes in function domains. The scheme’s function generator eliminates the coupling between model and gridded data sizes, enabling diverse multimedia generation and breaking resolution limits. A dedicated point cloud extractor is trained for adaptability. Through a literature review, this scheme is the first to perform message mapping in the functional domain. During the experiment, taking images as an example, methods such as PSNR, StegExpose, and neural pruning were used to demonstrate that the generated image quality is almost indistinguishable from the real image. At the same time, the generated image is robust. The accuracy of message extraction can reach 96.88% when the embedding capacity is 1 bpp, 89.84% when the embedding capacity is 2 bpp, and 82.21% when the pruning rate is 0.3. 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

The exponential growth of multimedia communication necessitates advanced techniques for secure data transmission. This paper details a new data hiding method centered on a predictive voting mechanism that leverages neighboring pixels to estimate a pixel’s value. Secret data are concealed within these predictions via a purpose-built lookup table, and the retrieval process involves re-estimating the predicted pixels and applying an inverse mapping function. Experimental results demonstrate that the proposed method achieves an embedding capacity of up to 686,874 bits, significantly outperforming previous approaches while maintaining reliable data recovery. Compared with existing schemes, our approach offers improved performance in terms of both embedding capacity and extraction accuracy, making it an effective solution for robust multimedia steganography. Full article

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

For the process of covert transmission of text information, in addition to the need to ensure the quality of the text at the same time, it is also necessary to make the text content match the current context. However, the existing text steganography methods excessively pursue the quality of the text, and lack constraints on the content and emotional expression of the generated steganographic text (stegotext). In order to solve this problem, this paper proposes an emotionally controllable text steganography based on large language model and named entity. The large language model is used for text generation to improve the quality of the generated stegotext. The named entity recognition is used to construct an entity extraction module to obtain the current context-centered text and constrain the text generation content. The sentiment analysis method is used to mine the sentiment tendency so that the stegotext contains rich sentiment information and improves its concealment. Through experimental validation on the generic domain movie reviews dataset IMDB, the results prove that the proposed method has significantly improved hiding capacity, perplexity, and security compared with the existing mainstream methods, and the stegotext has a strong connection with the current context. 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