Mask FORD-NET: Efficient Detection of Digital Image Forgery using Hybrid REG-NET based Mask-RCNN

Digital image is a binary representation of visual data which provides a rapid method for analyzing large quantities of data. Furthermore, digital images are more vulnerable to fraud when distributed over an open channel via information and communication technology. However, the image data can be modified fraudulently by intruders using vulnerabilities in telecommunications infrastructure. To overcome these issues, this paper proposes a novel Mask-RCNN based Image FORgery Detection (Mask FORD-NET) which is developed for digital image forgery detection. Initially, the input image is passed beyond the recompression module to reduce the insignificance and complexity of the image to preserve or transfer the data efficiently. After image recompression, the recompressed image is transferred to the feature extraction phase which is done by using REG-NET. The extracted features are received to the noise cancellation and ELA converter module to analyze and reduce the ambient noise. After noise cancellation, the data are passed to the MASK-RCNN module, to detect and classify the forged images and finally provide the segmented output. The Mask FORD-NET framework is simulated by using MATLAB. The efficiency of the proposed Mask FORD-NET framework is assessed by using accuracy, precision, recall, and F1-measure. The experimental results show that the accuracy of the Mask FORD-NET framework has increased to up to 98.72% for digital image forgery detection. The accuracy of the proposed Mask FORD-NET framework is 80.72%, 86.32%, and 95.00% better than existing ASCA, VixNet, and MiniNet techniques respectively.