A CONTENT-ADAPTIVE ±1-BASED STEGANOGRAPHY BY MINIMIZING THE DISTORTION OF FIRST ORDER STATISTICS

Least significant bit (LSB) matching is a well-known steganographic method with advantages of high payload, good visual/statistical imperceptibility and extreme ease of implementation. However, by utilizing the distortion of one-dimensional histogram or the generated additive embedding noise, some steganalyzers can perceive the existence of covert communication to some extent. Due to this, we extend the LSB matching steganography by minimizing the distortion of first order statistics (i.e., one-dimensional histogram) and adaptively embedding data into noise regions. With these extensions, our method significantly improves the stego-security. The experimental results also prove its superiority over some state-of-the-art steganographic methods against various steganalyzers.