Due to the increasing usage of multimedia and storage devices accessible, searching for large image databases has become imperative. Furthermore, the handiness of high-speed internet has escalated the exchange of images by users enormously. Content-Based Image Retrieval is proposed in this work, taking features based on Exact Legendre Moments, HVS color quantization with dc coefficient and statistical properties such as variance, mean, and skew of Conjugate Symmetric Sequency Complex Hadamard Transform (CS-SCHT). In most of the machine learning tasks, the quality of the learning process depends on dimensionality. High dimensional datasets can influence the classification outcome and training time. To overcome this problem, we use DE (Differential Evolution) to generate the optimal feature subsets. The features scaled by weights derived from the firefly algorithm, which fed to Multi-Class SVM. The fitness function taken for the firefly algorithm is the classification error of SVM. By minimizing fitness function, optimum weights are obtained. When these optimal weights are applied to SVM, the proposed algorithm exhibits better precision, recall, and accuracy when compared to some of the existing algorithms in the literature.

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.

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In this digital age, the extensive usage of digital devices and availability of open source image editing software leads to the easy manipulation of digital images. Copy-Move Forgery (CMF) is a guileless and widespread approach to hide or duplicate a certain portion of the image without leaving visual clues. Thus, it is difficult to detect the copy-move forgeries and there is a need for forensic experts to rely on an effective approach for CMF detection for forensic analysis. Hence, an efficient passive block based approach to detect and localize CMF is proposed. In the proposed method, texture features are extracted from Differential Excitation Component on the overlapping blocks of the image. Similarity measure is performed for block matching and mapping is done to identify the duplicated regions. Evaluation is performed qualitatively and quantitatively on CoMoFoD dataset; true detection rate of 0.99 and false detection rate of 0.08 has been achieved. Evaluation validates that proposed method out performs the other existing methods with regard to detection accuracy.

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