Big Data and Cognitive Computing

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Big Data Cogn. Comput., Volume 9, Issue 8 (August 2025) – 2 articles

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20 pages, 2026 KiB

Open AccessArticle

Synonym Substitution Steganalysis Based on Heterogeneous Feature Extraction and Hard Sample Mining Re-Perception

by Jingang Wang, Hui Du and Peng Liu

Big Data Cogn. Comput. 2025, 9(8), 192; https://doi.org/10.3390/bdcc9080192 - 22 Jul 2025

Abstract

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

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26 pages, 829 KiB

Open AccessArticle

Enhanced Face Recognition in Crowded Environments with 2D/3D Features and Parallel Hybrid CNN-RNN Architecture with Stacked Auto-Encoder

by Samir Elloumi, Sahbi Bahroun, Sadok Ben Yahia and Mourad Kaddes

Big Data Cogn. Comput. 2025, 9(8), 191; https://doi.org/10.3390/bdcc9080191 - 22 Jul 2025

Abstract

Face recognition (FR) in unconstrained conditions remains an open research topic and an ongoing challenge. The facial images exhibit diverse expressions, occlusions, variations in illumination, and heterogeneous backgrounds. This work aims to produce an accurate and robust system for enhanced Security and Surveillance. A parallel hybrid deep learning model for feature extraction and classification is proposed. An ensemble of three parallel extraction layer models learns the best representative features using CNN and RNN. 2D LBP and 3D Mesh LBP are computed on face images to extract image features as input to two RNNs. A stacked autoencoder (SAE) merged the feature vectors extracted from the three CNN-RNN parallel layers. We tested the designed 2D/3D CNN-RNN framework on four standard datasets. We achieved an accuracy of

98.9 %

. The hybrid deep learning model significantly improves FR against similar state-of-the-art methods. The proposed model was also tested on an unconstrained conditions human crowd dataset, and the results were very promising with an accuracy of

95 %

. Furthermore, our model shows an 11.5% improvement over similar hybrid CNN-RNN architectures, proving its robustness in complex environments where the face can undergo different transformations. Full article

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Big Data Cogn. Comput., Volume 9, Issue 8 (August 2025) – 2 articles

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