Frequency domain manipulation of multiple copy-move forgery in digital image forensics

Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move forgery detection, conventional approaches are generally effective at identifying simple multiple copy-move forgeries. However, the conventional approaches and deep learning approaches often fall short in detecting multiple forgeries when transformations are applied to the copied regions. Motivated from these findings, a transform domain method for generating and analyzing multiple copy-move forgeries is proposed in this paper. This method utilizes the discrete wavelet transform (DWT) to decompose the original and patch image into approximate (low frequency) and detail coefficients (high frequency). The patch image approximate and details coefficients are inserted into the corresponding positions of the original image wavelet coefficients. The inverse DWT (IDWT) reconstructs the processed image planes after modification which simulates the multiple copy move forgery. In addition, this approach is tested by resizing the region of interest with varying patch sizes resulting in an interesting set of outcomes when evaluated against existing state-of-the-art techniques. This evaluation allows us to identify gaps in existing approaches and suggest improvements for creating more robust detection techniques for multiple copy-move forgeries.