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The main problem encountered during multimedia transmission is its protection against illegal distribution and copying. One of the possible solutions for this is digital watermarking. Digital audio watermarking is the technique of embedding watermark content to the audio signal to protect the owner copyrights. In this paper, we used three wavelet transforms i.e. Discrete Wavelet Transform (DWT), Double Density DWT (DDDWT) and Dual Tree DWT (DTDWT) for audio watermarking and the performance analysis of each transform is presented. The key idea of the basic algorithm is to segment the audio signal into two parts, one is for synchronization code insertion and other one is for watermark embedding. Initially, binary watermark image is scrambled using chaotic technique to provide secrecy. By using QuantizationIndex Modulation (QIM), this method works as a blind technique. The comparative analysis of the three methods is made by conducting robustness and imperceptibility tests are conducted on five benchmark audio signals.

With the increase of modest technology, copy-move forgery detection has grown in a rapid rate that new era of forged images came true which has the same resemblance as the old ones i.e. difficult to find out with naked human perception. Fake currency detection is one in the effect that currency note is tampered in a way such it has the similar resemblance as the original one. So in order to find out the duplicate or forged portion of the image we go for different splicing algorithms using different techniques. Image forgery results to various security issues. Hence an efficient algorithm is required to detect the forgery in images. By using DCT algorithm blocks of the image are represented by DCT coefficients. Presence of blocking articrafts in DCT makes the method to be a drawback. Hence we propose DWT for segmentation of image. Lexicographical sorting is utilized to find out the cloned image blocks. Finally normalization is applied to find the distance in between similar vectors. In DWT provides better resolution and segmentation compared with DCT. In this paper, due to DWT, Image Forgery detection is done on lowlevel image representation. By using DWT better accuracy in finding out the forgery is achieved in a less time which gradually reduces complexity.