AI-Powered Steganographic Techniques: A Comparison of Traditional Methods and Modern Machine Learning Approaches

Steganography is pivotal for covertly embedding sensitive data in digital media to evade unauthorized detection. Traditional methods like LSB substitution and DCT are increasingly susceptible to detection due to evolving analysis and detection technologies. In contrast, AI-driven approaches such as CNNs and GANs offer significant advancements in steganography by enhancing data concealment and detection resilience. This study comprehensively compares traditional techniques (LSB, DCT) with AI-based methods (CNNs, GANs) using metrics like PSNR, SSIM, and detection accuracy. Results indicate that while traditional methods are simple to implement, AI-based techniques excel in detection resistance and data hiding quality. Specifically, DCT achieves superior image fidelity with high PSNR (36.244 to 96.954) and nearly perfect SSIM, making it ideal for robust data concealment. GANs generate images closely resembling originals with moderate PSNR (8.341 to 14.740) and SSIM (0.199 to 0.812), offering strong detection evasion albeit demanding substantial computational resources. This research emphasizes the importance of method selection tailored to specific needs, balancing computational complexity, detection resilience, and image fidelity. Future work should focus on optimizing AI algorithms for efficiency and addressing ethical implications in deploying advanced steganographic techniques.