Improving DCT-based JPEG Steganography using Adaptive LSB Matching for Resistance to Entropy-based Detection

Data transmission security has become a critical issue in the digital era, where steganography plays an important role in concealing confidential information within digital media. A key limitation of conventional Discrete Cosine Transform (DCT)-based steganography in JPEG images is its vulnerability to statistical detection through entropy analysis, as well as the risk of significant degradation in visual quality. This study aims to enhance DCT-based steganography techniques to minimize entropy-based detection while maintaining an optimal Peak Signal-to-Noise Ratio (PSNR). The proposed method employs an Adaptive LSB Matching approach by embedding messages into low-to-mid frequency coefficients using an adjustment mechanism (x±1). The performance of this method is then compared with the standard DCT approach. Experimental results show that the proposed method is able to preserve visual quality, achieving an average PSNR of 40.41 dB under maximum payload conditions, while reducing the entropy difference (ΔH) to 0.00251. These findings demonstrate that the developed technique is more robust against statistical steganalysis attacks and provides better visual fidelity compared to conventional methods.