AGFP: A Deep Attention-Guided Framework for DWT-Based Image Steganography

Abstract

This study introduces a novel attention-guided Discrete Wavelet Transform (DWT)-based steganography framework, named Attention-Guided Feature Perturbation (AGFP), which integrates deep visual attention maps with transform-domain embedding to enhance imperceptibility, robustness, and steganalysis resistance. Unlike recent deep-learning-based steganographic systems such as iSCMIS, JARS-Net, and RMSteg, which achieve high visual fidelity but are susceptible to statistical detection, AGFP perturbs only those wavelet coefficients that are identified as perceptually and statistically stable by attention mechanisms extracted from pre-trained CNN models (VGG19, ResNet50, AlexNet, and GoogLeNet). The proposed method is evaluated on the USC-SIPI dataset and the BOSSBase 1.01 benchmark. Experimental results show that AGFP achieves PSNR values between 64.29 and 55.43 dB and SSIM scores between 0.9999 and 0.9989 across varying payloads, indicating consistently high visual quality. While iSCMIS reports slightly higher PSNR and SSIM values, AGFP significantly outperforms all compared methods in bit error rate (BER)—achieving 0.01–0.12, compared to 0.45–0.47 for iSCMIS, 0.31–0.37 for RMSteg, and 0.57–0.75 for JARS-Net. Furthermore, AGFP attains the lowest RS, SPA, and SRM steganalysis detection scores among both classical and deep-learning-based systems. These results confirm that AGFP offers a more balanced and secure steganographic solution, combining high imperceptibility with substantially enhanced robustness and detectability resistance, positioning it as a strong alternative to recent deep-learning-based steganographic frameworks.