Advancements and Challenges in Image Steganographer: A Comprehensive Review

Image steganography, a combination of computer vision and encryption, is a classic challenge for hiding information in cover images for covert communication. This review paper examines conventional and modern image steganography approaches, including key issues and advancements. Explore the classical tension between concealing maximum information and avoiding discovery, stressing payload capacity in steganographic algorithms. Dissecting traditional methods like embedding RAR archives in JPEG files reveals weaknesses to third-party changes that risk hidden data. Image domain, transform domain, and file-format-based steganography approaches are described, along with their pros and cons. Image domain methods, such as the Least Significant Bit (LSB) method, are widely used for covert information transfer via pixel-level statistical changes. Modern advances include deep learning in image steganography. End-to-end auto encoder-based models show promising embedding capacity and durability against passive attacks. The study emphasizes the complex relationship between deep steganography and security issues by highlighting adversarial situations and their possible susceptibility to assaults. A visual representation of a common encoder-decoder network for deep steganography models shows attack channels for deleting or changing secret images and the usual path for correct image recovery. The article indicates that steganography algorithms must balance payload capacity, detection robustness, and adaptability to cover image patterns. This paper covers the progression from classical to deep learning-based image steganography and the associated issues that pave the way for future research..