Image Steganography Based on Deep Learning

Abstract

: Image steganography aims to embed secret information by modifying carrier images while maintaining visual invisibility, which holds significant value in fields such as information security and copyright protection. Traditional steganographic methods are constrained by manually designed rules, suffering from insufficient concealment and robustness. The development of deep learning has promoted the innovation of steganographic techniques. This paper conducts research from four dimensions: generative steganography, adaptive steganography, adversarial steganography, and reversible neural network steganography. Generative steganography directly synthesizes stego carriers through generative models, offering advantages of high concealment and conformity to natural data distribution; adaptive steganography dynamically adjusts embedding strategies according to carrier content to balance concealment and capacity; adversarial steganography utilizes adversarial training to optimize models, enabling generated stego carriers to deceive steganalysis tools and enhance anti-detection capabilities; reversible neural network steganography, based on reversible network structures, is suitable for scenarios with strict requirements for data integrity. The integration of deep learning and steganographic techniques provides technical support for emerging scenarios such as semantic communication, and holds important theoretical and practical significance for promoting the intelligent and robust development of the information hiding field.