Comparative results with existing state-of-the-art methods show that this end-to-end embedding approach delivers promising performance in terms of human visual accuracy, with a PSNR score of 37.054 and an SSIM score of 0.9754.
Abstract
The increased rate of cyber threats such as fraud and other forms of attack, including phishing, malware, and denial-of-service attacks, has led to a growing demand for secure methods to ensure the security of sensitive information that is transferred among users. Video steganography has become a very important element in dealing with these security issues, as communication has become more dependent on multimedia content. Hiding information is now an area that is being developed very fast with the introduction of Deep Learning (DL)-based steganography methods. This framework introduces Vid_Steg_DenseUNet, a video steganography method which efficiently extracts multi-level features using edge-preserving U-Net and DenseNet through Invertible Neural Networks (INN) interactions. The main objectives of this approach include improving the perceptual fidelity of the stego video and enhancing the quality of the recovered secret image. These improvements are evaluated using objective imperceptibility metrics through pixel-level and structural analyses, such as PSNR and SSIM. The model was trained on the DIV2K images and Ultra Video Group (UVG) videos. The proposed model hides a secret color image in each video frame, and its performance is assessed using quantitative difference metrics. Comparative results with existing state-of-the-art methods, such as recent DL-based video steganography frameworks, show that this end-to-end embedding approach delivers promising performance in terms of human visual accuracy, with a PSNR score of 37.054 and an SSIM score of 0.9754. The results also demonstrate that the model achieves enhanced security and high resistance to detection techniques.
A secure image steganography framework that integrates Hybrid Edge-Guided Fourier-Domain Steganography Framework with the Convolutional Neural Network (CNN) based learned detector for securing the process of image steganography is proposed.
S. S., Meruvu Sai Kumar· International Journal of Sci...· 0 citations
As the digital landscape continues to expand, the secure concealment and retrieval of information have become equally critical for ensuring confidentiality and integrity. Steganography and steganalysis serve as a cornerstone for securing and retrieving sensitive information and facilitating covert communication, while effectively mitigating potential threats. Despite significant advancements in digital communication technologies, ensuring information security remains an ongoing challenge, particularly in the context of neural network (NN)‐based steganography and steganalysis architectures. This study presents a comprehensive analysis of steganography and steganalysis methods, evolving from classical to advanced intelligence frameworks by emphasizing image quality, security of hidden information, payload capacity, and robust detection mechanisms against adversarial perturbations. By leveraging state‐of‐the‐art methods such as convolutional neural network (CNN), generative adversarial network (GAN), autoencoders, and fuzzy logic (FL) for security, imperceptibility, robustness, and the ability to address ongoing challenges related to payload capacity, adaptive information embedding and retrieval, and computational efficiency. This study explores the fields of steganography and steganalysis, focusing on embedding and extraction methods from conventional methods (non‐neural network [N‐NN]) to NN architectures. The insights presented serve as a foundational reference for transitioning from conventional embedding and decoding approaches to advanced NN‐driven techniques, thereby enhancing security, efficiency, and resilience in covert communication systems.
Unknown authors· WIREs Data Mining and Knowle...· 0 citations
Deep learning-based video steganography has made significant strides, yet conventional explicit methods often suffer from cover distortion and reduced extraction accuracy at high capacities. In this paper, we propose an implicit video steganography framework that treats video hiding and recovery as a dual-stream generation process leveraging implicit neural representations. Instead of altering existing carriers, secret information is encoded within the neural network’s weights, making it an inherent part of the generation process. We introduce a dual-stream input encoding mechanism that decouples the input space into temporal and cryptographic encodings to ensure covert transmission, allowing only authorized receivers to recover hidden content. Furthermore, a multi-scale generation network, incorporating frequency-aware upscaling and statistical distribution loss, is presented to achieve high-quality reconstruction. Extensive experiments demonstrate that our approach achieves state-of-the-art results, minimizing detectable discrepancies while concealing up to seven secret videos within a single carrier. Our method significantly outperforms existing benchmarks by a margin of over 10 dB in peak signal-to-noise ratio, highlighting its superior imperceptibility, accuracy, and security.
Yifei Wang, Gaozhi Liu, Sheng Li et al.· Computer/law journal· 0 citations
The growing requirement for secure multimedia communication has tested typical video steganography methods, which often trade off embedding capacity, visual quality, and signal-processing robustness. This study proposes an adaptive Discrete Wavelet Transform (DWT)-based video steganography architecture that uses key-based pseudo-random frame selection and adaptive coefficient embedding to hide data safely and undetected. Two layers of Haar DWT deconstruct selected video frames, and a dynamic scaling factor based on local and global statistical parameters encodes hidden information in LH and HL sub-bands. We test the proposed system on the UCF101 dataset of human action videos under Gaussian noise, JPEG compression, filtering, and frame dropping. Experimental results demonstrate that the recommended approach generates a high peak signal to noise ratio (PSNR) of 43.85 dB and a low MSE of 4.12, resulting in excellent visual clarity with reduced distortion. The suggested framework has a maximum embedding capacity of 0.58 bpp, outperforming LSB, DCT, and DWT. Robustness analysis shows a bit error rate < 0.02 with common attacks, ensuring data recovery and signal resilience. Results reveal that the adaptive DWT design balances security, embedding efficiency, and visual quality, making it suitable for secure multimedia transmission and digital content protection.
Unknown authors· International Journal of Com...· 0 citations
A new efficient technique for hiding a gray image inside a blue layer of color image is presented by combining the genetic algorithm (GA) with the Least Significant Bit (LSB) approach to optimal solution permutation for embedding pixel assortment of the image where data is to be concealed.
Dena Abu Laila, Ziad Dawahdeh, Amer Alqutaesh et al.· Journal of Cyber Security an...· 0 citations
VisionStego is proposed, a duallayer security architecture that pairs symmetric-key encryption with an artificial-intelligence-guided steganographic embedding stage, so that cloud-hosted data is protected in both substance and appearance.
R. Saxena, Priti Maheshwary· International Journal for Re...· 0 citations
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