Abstract
Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies a kNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the 5th-7th bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images.
Highlights
I N the digital era, people must pay much attention to privacy protection when communicating on the Web
Our experiments demonstrate that our algorithm can restore embedded images with good visual quality, and outperforms the compared algorithms
Our algorithm firstly finds those unreliable bits of every pixel extracted from the stego-image and conducts bit approximation with those bits of its k nearest neighbor [39], [40], [41] pixels
Summary
I N the digital era, people must pay much attention to privacy protection when communicating on the Web. As digital images are corrupted by interchannel noise [5], [6] or active noise attack, visual quality of the secret image extracted/decrypted from corrupted stego-image is inevitably hurt To overcome this problem, a possible strategy is to use filtering algorithms [7]. The existing filtering algorithms have shown good performance in noise removal for those images directly contaminated, but they are ineffective in recovering/restoring embedded image from corrupted stego-image due to the following fact. For embedded image extracted from corrupted stego-image, a dirty/noisy pixel can contain both reliable and unreliable bits together. This indicates that noise removal in embedded image should be done by bit approximation instead of pixel replacement.
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