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This paper presents a robust image watermarking method based on geometric modeling. In this method, nine samples of the approximation coefficient of the image blocks are utilized to construct a plane in the 3-D space. We change the dihedral angle formed between the created plane and the x-y plane for data embedding. In order to preserve the imperceptibility of the watermark, geometrical computations are used to minimize the embedding distortion. Due to embedding in the dihedral angle between two planes, the proposed scheme has high robustness to gain attacks. Besides, by using the low frequency components of the image blocks for data embedding, high robustness against noise and compression attacks has been achieved. Experimental results confirm the validity of the theoretical analysis given in this paper and show the superiority of the method over similar techniques in this field.

Steganalysis are used to detect whether an image contains a hidden message. By analyzing different image features between stego images and cover-images, a steganalyzer is able to detect stego images. In this paper, we present a new method in LSB embedding by avoiding the change of statistic features. After embedding data in the first LSB bits, we apply Particle Swarm Optimization by adjusting the second LSB bits of a stego image while creating the desired statistic features to generate the modified stego images that can break the inspection of chi-square attack. Experimental results show that our algorithm can not only pass the detection of chi-square test, but also leave our hidden message unchanged on the first LSB bit, and enhance the peak signal-to-noise ratio of stego images. By using PSO algorithm, speed of convergence also is improved.