Real-time image carrier generation based on generative adversarial network and fast object detection

Li, Chuanlong; Sun, Xingming; Zhou, Zhili; Yang, Yimin

doi:10.1007/s11554-020-00969-w

Cited by 2 publications

(1 citation statement)

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“…e main idea is to minimize the heuristically defined embedding distortion to achieve high undetectability [16]. In recent years, GAN (generative adversarial network) is gradually being combined with traditional steganography [17][18][19][20]. ese methods are still modification based steganography, in which the modification traces will inevitably be left during embedding and be detected by a modern steganalyzer [21].…”

Section: Introductionmentioning

confidence: 99%

Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

Wang

Song

et al. 2021

Security and Communication Networks

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The deep learning based image steganalysis is becoming a serious threat to modification-based image steganography in recent years. Generation-based steganography directly produces stego images with secret data and can resist the advanced steganalysis algorithms. This paper proposes a novel generation-based steganography method by disguising the stego images into the kinds of images processed by normal operations (e.g., histogram equalization and sharpening). Firstly, an image processing model is trained using DCGAN and WGAN-GP, which is used to generate the images processed by normal operations. Then, the noise mapped by secret data is inputted into the trained model, and the obtained stego image is indistinguishable from the processed image. In this way, the steganographic process can be covered by the process of image processing, leaving little embedding trace in the process of steganography. As a result, the security of steganography is guaranteed. Experimental results show that the proposed scheme has better security performance than the existing steganographic methods when checked by state-of-the-art steganalytic tools, and the superiority and applicability of the proposed work are shown.

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