Extraction of Secret Message Based on Uniformly Most Powerful Test

As the ultimate goal of steganalysis, secret message extraction is a bottleneck and difficulty that has long plagued the development of steganalysis technology. Existing pioneering work on secret message extraction for adaptive steganography based on STC (Syndrome-Trellis Codes): the method based on randomness test under plaintext embedding may misjudge incorrect steganographic key as correct steganographic key, resulting in the failure of extraction. For this reason, a method for extracting message with 100% accuracy for plaintext embedding is proposed in this manuscript. First, by analyzing the probability distribution of stego images, a theorem is given: For STC-based adaptive steganography algorithm, the sequences extracted by correct and incorrect steganographic keys are statistically different. Then based on this theorem, a uniformly most powerful test model is designed to recover the correct steganographic key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method