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Preemptive Image Protection against Steganography | IEEE Conference Publication | IEEE Xplore

Preemptive Image Protection against Steganography

Publisher: IEEE

Abstract:

Image steganography plays a vital role in the realm of information hiding, involving the discreet embedding of confidential data within an image. However, the misuse of t...View more

Abstract:

Image steganography plays a vital role in the realm of information hiding, involving the discreet embedding of confidential data within an image. However, the misuse of this technology by malicious actors on social media platforms poses a significant threat to public security. Presently, the primary defense against image steganography is steganalysis technology, which effectively distinguishes steganographic images from regular ones by detecting subtle modifications. Nevertheless, there is considerable room for improvement in existing steganalysis techniques, especially when dealing with small payloads. In this paper, we propose a proactive strategy to enhance the detection of steganographic images with small payloads. Specifically, as the image is captured, we apply a meticulous-designed and imperceptible mask to it. This mask is aimed at increasing the success rate of detection. We have developed three convolutional neural networks (CNNs) to facilitate this proactive image processing: preemptive-CNN, steganography network (Stego-CNN), and steganalysis discriminator. During the training process, Stego-CNN remains unaltered, while preemptive-CNN and the discriminator are jointly trained to ensure that the applied mask enhances the detection accuracy. Experimental results demonstrate the effectiveness of our proposed approach in enhancing the detection accuracy, all without compromising the visual quality of the images.
Date of Conference: 31 October 2023 - 03 November 2023
Date Added to IEEE Xplore: 20 November 2023
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Publisher: IEEE
Conference Location: Taipei, Taiwan

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