Abstract
As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various types of images are mixed up in the social networks, and it is difficult for the servers of social networks to detect whether the images are clean. To prevent the illegal communication, this paper proposes a method of defeating data hiding by removing the secret data without impacting the original media content. The method separates the clean images from illegal images using the generative adversarial network (GAN), in which a deep residual network is used as a generator. Therefore, hidden data can be removed and the quality of the processed images can be well maintained. Experimental results show that the proposed method can prevent secret transmission effectively and preserve the processed images with high quality.
Highlights
With the fast development of information technology, the online social networks (OSN) can provide us a convenient transmission of various messages
4.1 Experimental setting We test three classic watermarking algorithms based on quantized index modulation (QIM), spread spectrum (SS), and uniform log-polar mapping (ULPM), respectively
The above-mentioned three watermarking algorithms are utilized to generate watermarked image denoted as φQIM, φSS, and φULPM
Summary
With the fast development of information technology, the online social networks (OSN) can provide us a convenient transmission of various messages. One possible solution is to interfere with the image content in OSN and destroy the hidden data that might be embedded. Steganography is fragile to common attacks, and hidden data can be removed . It is difficult to remove the messages hidden by robust steganography or watermarking tools. As most data-hiding methods can be viewed as adding noises, it would be useful to remove the hidden data by image denoising. General steganography algorithms are not robust, in other words, social networks can break the secret information of stego images. The above three watermarking algorithms have difference in robustness, they will not fail in the face of lightweight image processing in social networks. Our method can prevent illegal communication by using of robust watermarking, so as to break the hidden data that cannot be influenced by traditional attacks. The method can be regarded as a new evaluation for the robustness of information hiding
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