Adaptive Channel Recalibration for Deep Learning-Driven Steganalysis

Steganalysis is the detection of hidden messages within digital media. Steganalysis techniques have witnessed significant advancements with the integration of deep learning techniques. Traditional methods which rely on manual or handcrafted features and machine learning classifiers have demonstrated effectiveness but were limited in the capability of generalization and adaptability to diverse steganographic schemes. Recent deep learning-based steganalysis approaches leverage Convolutional Neural Networks (CNNs) to automate feature extraction, but their ability to detect subtle steganographic noise remains a challenge. This paper proposes an Adaptive Channel Recalibration (ACR) mechanism to enhance deep learning-driven steganalysis. Adaptive Channel Recalibration dynamically assigns importance scores to feature maps, allowing the network to selectively emphasize discriminative features while suppressing irrelevant activations. By integrating ACR within a deep residual network (ResNet) framework, the model’s ability to detect weak steganographic signals has been improved while maintaining computational efficiency. The proposed approach is evaluated on the BOSSbase 1.01 dataset with various steganographic methods, including WOW, S-UNIWARD, and HILL. Experimental results demonstrate that this method outperforms existing steganalysis techniques, including Green Steganalyzer and Graph Attention Steganalysis, achieving superior detection accuracy and robustness. This work highlights the potential of adaptive feature recalibration in improving steganalysis performance and sets a foundation for further research in deep learning-driven steganalysis.