Yuwei Lu^1,2, Jia Liu^1,2,*, Qiya Wang^1,2, Yujie Liu^1,2, Peng Luo^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084506 - 13 August 2026

Abstract Existing deep-learning-based steganography methods are typically designed for single-modality cover data and often rely on modality-specific network structures, which limits their cross-modal adaptability. To address this limitation, this paper proposes a multimodal implicit neural representation (INR) steganographic framework based on a point-cloud intermediate representation. The framework first fits the cover data as a carrier INR and samples the fitted carrier into a noisy point cloud. A pre-shared noise seed and secret key are then used to reproduce the carrier-derived point cloud and select a key-dependent point subset as the secret point cloud. Finally, a separate… More >

Islam T. Almalkawi^1,*, Samer Khasawneh¹, Hamza M. Alkhatib¹, Sabya Shtaiwi¹, Rami Halloush², Manel Guerrero Zapata³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.077025 - 13 August 2026

Abstract Current image steganography methods often struggle to balance security, payload capacity, and computational efficiency, with many spatial-domain techniques vulnerable to statistical steganalysis and complex methods incurring high overhead. To address persistent challenges in secure data communication, this paper introduces a novel hybrid chaotic-based multi-layered image security and steganography scheme to enhance resistance against detection while offering adaptable performance. The proposed scheme first integrates Fisher-Yates permutation driven by a Logistic Map PRNG, followed by stream cipher encryption using a Hénon Map-generated keystream to secure the secret image. Embedding is then performed via a unique three-pass chaotic More > Graphic Abstract

Qiya Wang^1,2, Jia Liu^1,2,*, Yuwei Lu^1,2, Yujie Liu^1,2, Peng Luo^1,2

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081589 - 23 July 2026

Abstract Implicit Neural Representation (INR) is a technique that models continuous signals using neural networks, replacing traditional discrete grid representations with a coordinate-to-value mapping function. As a data carrier, INR is gradually being adopted as the target for steganographic processing. However, existing INR-based steganographic schemes typically require modifying network structures (e.g., weights, nodes) and retraining to obtain stego INRs, leading to high time consumption and the need for re-training when replacing cover images. To address this issue, this paper proposes StegaMIR (Steganography via Modulated Implicit Representations), an image steganographic scheme based on modulated implicit representations. It… More >

Mohammad Muzayyin Amrulloh^1,2, Tohari Ahmad^1,*, Royyana Muslim Ijtihadie¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079951 - 15 June 2026

Abstract The rapid development of digital technology has facilitated data exchange and communication, while simultaneously increasing security threats such as data theft and manipulation. As personal data is highly confidential, effective protection mechanisms are required in the digital era. Audio steganography hides secret messages (payload) within audio signals; however, many existing approaches rely on a single stego-audio output, which can lead to information leakage during storage or transmission if the file is intercepted. This vulnerability allows an attacker to more easily reconstruct the steganographic scheme from a single output. To address this limitation, this study proposes… More >

Mangala Natampalli^1,2,*, Kruthika Gottikere Channagangaiah³, Bodi Eswara Reddy¹, Rajkumar Buyya⁴, Venugopal Kupanna Rajuk³, Sundaraja Sitharama Iyengar⁵, Lalit Mohan Patnaik⁶

Journal on Internet of Things, Vol.8, pp. 1-30, 2026, DOI:10.32604/jiot.2026.067726 - 24 February 2026

Abstract Contemporary banking focuses on self-service and customer-centric experience by harnessing Internet of Things (IoT) and Cloud Computing. However, these systems remain vulnerable to multifaceted cyberattacks. The IoT-Cloud-based systems can be safeguarded through authentication, confidentiality, integrity, availability, privacy, and non-repudiation. This work proposes a multi-stage security consisting of Generative Adversarial Networks (GAN) for facial authentication, hybrid Curvelet and Least-Significant-Bit (LSB) Steganography for data protection, and Ethereum Blockchain for transactions and storage security to provide complete protection to the self-service pipeline. Customers are authenticated using live images from IoT cameras by GAN facial recognition. Improved data concealment… More >

Yi Cao¹, Kuo Zhang¹, Chengsheng Yuan^2,*, Linglong Zhu¹, Wentao Ge²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070158 - 09 December 2025

Abstract Generative steganography uses generative stego images to transmit secret message. It also effectively defends against statistical steganalysis. However, most existing methods focus primarily on matching the feature distribution of training data, often neglecting the sequential continuity between moves in the game. This oversight can result in unnatural patterns that deviate from real user behavior, thereby reducing the security of the hidden communication. To address this issue, we design a Gomoku agent based on the AlphaZero algorithm. The model engages in self-play to generate a sequence of plausible moves. These moves form the basis of the… More >

Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4995-5013, 2025, DOI:10.32604/cmc.2025.068429 - 23 October 2025

Abstract Ensuring the secure transmission of secret messages, particularly through video—one of the most widely used media formats—is a critical challenge in the field of information security. Relying on a single-layered security approach is often insufficient for safeguarding sensitive data. This study proposes a triple-lightweight cryptographic and steganographic model that integrates the Hill Cipher Technique (HCT), Rotation Left Digits (RLD), and Discrete Wavelet Transform (DWT) to embed secret messages within video frames securely. The approach begins with encrypting the secret text using a private key matrix (PK₁) of size 2 × 2 up to 6 × 6… More >

Da-Chun Wu^*, Bing-Han Sie

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 535-580, 2025, DOI:10.32604/cmes.2025.067239 - 31 July 2025

Abstract Data hiding methods involve embedding secret messages into cover objects to enable covert communication in a way that is difficult to detect. In data hiding methods based on image interpolation, the image size is reduced and then enlarged through interpolation, followed by the embedding of secret data into the newly generated pixels. A general improving approach for embedding secret messages is proposed. The approach may be regarded a general model for enhancing the data embedding capacity of various existing image interpolation-based data hiding methods. This enhancement is achieved by expanding the range of pixel values… More >

Yixin Tang^1,2, Minqing Zhang^1,2,3,*, Peizheng Lai^1,2, Ya Yue^1,2, Fuqiang Di^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5733-5750, 2025, DOI:10.32604/cmc.2025.064901 - 30 July 2025

Abstract Traditional steganography conceals information by modifying cover data, but steganalysis tools easily detect such alterations. While deep learning-based steganography often involves high training costs and complex deployment. Diffusion model-based methods face security vulnerabilities, particularly due to potential information leakage during generation. We propose a fixed neural network image steganography framework based on secure diffusion models to address these challenges. Unlike conventional approaches, our method minimizes cover modifications through neural network optimization, achieving superior steganographic performance in human visual perception and computer vision analyses. The cover images are generated in an anime style using state-of-the-art diffusion More >

Lingyun Xiang^*, Xu He, Xi Zhang, Chengfu Ou

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025

Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >