A robust associative watermarking technique based on vector quantization

In this paper, the concepts of vector quantization (VQ) and association rules in data mining are employed to propose a robust watermarking technique. Unlike ordinary or traditional watermarking techniques, our approach hides association rules of the watermark, instead of the whole watermark; in other words, the embedded information is the association rules of the watermark. First, VQ encoding is performed on the original image and watermark to generate the index tables, and from which association rules are further mined. Subsequently, by embedding the association rules of the watermark into the association rules of the original image, the purpose for watermarking is accomplished. Finally, VQ decoding technique is applied to reconstruct the watermarked image from the watermarked index table. Experimental results show that our proposed method achieves effective resistance against several image processings such as blurring, sharpening, adding in Gaussian noise, cropping, and JPEG lossy compression. Moreover, the embedding capacity is also significantly increased, so any a complex watermark image is still acceptable in this method.