Introduction -- Background Study and Analysis -- Copy-Move Forgery Detection using Local Binary Pattern Histogram Fourier Features -- Blur Invariant Block-based CMFD System using FWHT Features -- Geometric Transformation Invariant Improved Block based Copy-Move Forgery Detection -- Key-points based Enhanced Copy-Move Forgery Detection System using DBSCAN Clustering Algorithm -- Image Copy-Move Forgery Detection using Deep Convolutional Neural Networks.

This book presents a detailed study of key points and block-based copy-move forgery detection techniques with a critical discussion about their pros and cons. It also highlights the directions for further development in image forgery detection. The book includes various publicly available standard image copy-move forgery datasets that are experimentally analyzed and presented with complete descriptions. Five different image copy-move forgery detection techniques are implemented to overcome the limitations of existing copy-move forgery detection techniques. The key focus of work is to reduce the computational time without adversely affecting the efficiency of these techniques. In addition, these techniques are also robust to geometric transformation attacks like rotation, scaling, or both.