With the advances in technology, humans tend to explore the world underwater in a more constructive way than before. The appearance of an underwater object varies depending on depth, biological composition, temperature, ocean currents, and other factors. This results in colour distorted images and hazy images with low contrast. To address the aforesaid problems, in proposed approach, initially White balance algorithm is carried out to pre-process original underwater image. Contrast enhanced image is achieved by applying the Contrast Limited Adaptive Histogram Equalization algorithm (CLAHE). In CLAHE, tile size and clip limit are the major parameters that control the enhanced image quality. Hence, to enhance the contrast of images optimally, Firefly algorithm is adopted for CLAHE. Dark Channel Prior algorithm (DCP) is modified with guided filter correction to get the sharpened version of the underwater image. Multiscale fusion strategy was performed to fuse CLAHE enhanced and dehazed images. Finally, the restored image is treated with optimal CLAHE to improve visibility of enhanced underwater image. Experimentation is carried out on different underwater image datasets such as U45 and RUIE and resulted in UIQM = 5.1384, UCIQE = 0.6895 and UIQM = 5.4875, UCIQE = 0.6953 respectively which shows the superiority of proposed approach.

Digital Audio Watermarking involves the concealment of data within a discrete audio file. Intellectual property protection is the main driving force behind research in this area. So, an efficient yet simple audio watermarking algorithm in the frequency domain by embedding a visual watermark in the audio signal is proposed. Discrete Cosine Transform (DCT) along with Singular Value Decomposition (SVD) is used to embed the watermark image into the audio signal. Similarly, Discrete Wavelet Transform (DWT) and SVD is used as another technique. The performance of the two techniques is evaluated based on PSNR, MOS criteria and Pearson Correlation Coefficient (PCC). Experimental results show that the DCT-SVD method has good imperceptibility and is robust against different kinds of attacks such as noise adding, re-sampling, cropping.

RST Invariant Image Forgery Detection

A vision-based pedestrian and car tracking system which is able to distinguish between car and pedestrian is possible using Morphological processing and Blob analysis. Videos are sequence of image frames. Here the algorithm is developed to analyse a frame and the same will be applied to all frames in a video. The unwanted objects in video frame can be removed by converting colour frames into a gray scale and by applying thresholding algorithm. Threshold can be set depending on the object to be detected. Gray scale image will be converted to binary during thresholding process. Morphological processing will be applied on binary image to remove small unwanted objects that are presented in a frame. A developed blob analysis technique for extracted binary image facilitates pedestrian and car detection. Processing blob's information of relative size and location leads to distinguishing between pedestrian and car. The threshold, morphological and blobs process is applied to all frames in a video and finally original video with tagged cars will be displayed.

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.