• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권11호
• /
• pp.4153-4169
• /
• 2014

To recognize F5-like (such as F5 and nsF5) steganographic algorithm from multi-class stego images, a recognition algorithm based on the identifiable statistical feature (IDSF) of F5-like steganography is proposed in this paper. First, this paper analyzes the special modification ways of F5-like steganography to image data, as well as the special changes of statistical properties of image data caused by the modifications. And then, by constructing appropriate feature extraction sources, the IDSF of F5-like steganography distinguished from others is extracted. Lastly, based on the extracted IDSFs and combined with the training of SVM (Support Vector Machine) classifier, a recognition algorithm is presented to recognize F5-like stego images from images set consisting of a large number of multi-class stego images. A series of experimental results based on the detection of five types of typical JPEG steganography (namely F5, nsF5, JSteg, Steghide and Outguess) indicate that, the proposed algorithm can distinguish F5-like stego images reliably from multi-class stego images generated by the steganography mentioned above. Furthermore, even if the types of some detected stego images are unknown, the proposed algorithm can still recognize F5-like stego images correctly with high accuracy.

• 한국군사과학기술학회지
• /
• 제14권3호
• /
• pp.482-491
• /
• 2011

The purpose of this study is to design and implement a 2-level secret information transmission system which can be used for information hiding of images transmitted over various IT communication media. To increase the robustness of the hiding power, we combined the steganography method which inserts secret object into cover object to hide the very fact of information hiding itself, and the preprocessing stage to encrypt the secret object before the stego-insertion stage. As a result, even when the stego-image is broken by an attacker, the secret image is protected by encryption. We implemented the 2-level image insertion and extraction algorithm by using C++ programming language. Experiment shows that the PSNR values of stego-images of ours exceed 30.00db which is the threshold of human recognition. The methodology of this study can be applied broadly to the information hiding and protection of the military secret images.

• 방송공학회논문지
• /
• 제24권2호
• /
• pp.315-328
• /
• 2019

영상 스테그아날리시스는 입력 영상을 스테가노그래피 알고리즘이 적용된 스테고 영상과 스테가노그래피 알고리즘이 적용되지 않은 커버 영상으로 분류하는 알고리즘이다. 기존에는 주로 수제 특징 기반의 스테그아날리시스를 연구하였다. 하지만 CNN 기반의 물체 인식이 큰 성과를 이루면서 최근 CNN 기반의 스테그아날리시스가 활발히 연구되고 있다. CNN 기반의 스테그아날리시스는 물체 인식과는 달리 커버 영상과 스테고 영상의 미세한 차이를 식별하기 위해서 전처리 필터를 필요로 한다. 그러므로, CNN 기반의 스테그아날리시스 연구들은 효과적인 전처리 필터와 네트워크 구조를 개발하는 데 초점을 두고 있다. 본 논문에서는 동일한 실험 조건에서 기존 연구들을 비교하고, 그 결과를 기반으로 전처리 필터와 네트워크 구조적인 차이에 의한 성능 변화를 분석한다.