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This paper presents an adaptive image-watermarking technique based on just-noticeable distortion (JND) profile and fuzzy inference system (FIS) optimized with genetic algorithm (GA). Here it is referred to as the AIWJFG technique. During watermark embedding, it embeds a watermark into an image by referring the JND profile of the image so as to make the watermark more imperceptible. It employs image features and local statistics in the construction of an FIS, and then exploits the FIS to extract watermarks without original images. In addition, the FIS can be further optimized by a GA to improve its watermark-extraction performance remarkably. Experimental results demonstrate that the AIWJFG technique not only makes the embedded watermarks further imperceptible but also possesses adaptive and robust capabilities to resist on image-manipulation attacks being considered in the paper.

Most recent papers about visual cryptography for halftone images are dedicated to get a higher contrast decoded image. However, the hidden visual pattern often blends into the background image and leads to a confused image. In this paper, we propose an improved method for halftone image hiding. By using the proposed method, the background image can be eliminated and the hidden visual pattern can be revealed precisely. Experimental results show that the decoded visual patterns could reveal good visual quality under various kinds of input patterns. Furthermore, better visual quality can be obtained when more halftone images are overlaid.