Deep Learning-Based Digital Image Forgery Detection System

Abstract

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system.

Enhancing Digital Image Forgery Detection Using Transfer Learning

“…For splicing, [18] presented multiple image-splicing forgeries using Mask R-CNN and MobileNet-V1 backbone. A novel approach utilizing ResNet50v2 was introduced in [19], that considered image batches as an input and used YOLO CNN weights with ResNet50v2 architecture.…”

“…Tampered images in CASIA v2 were created by combining two different authentic images or using the same authentic image. Cropped parts underwent some processing including distortion, rotation, and scaling, to create an image that seems to be real, involving blurring the spliced region's edge [19].…”

A Thorough Investigation on Image Forgery Detection

“…There are some other model which exploits the deep learning based approach such as DLFM-CMDFC [139], deep learning by recompression [140], copy-move image forgery [141], CNN by using the architecture of ResNet50v2 [142].…”

Copy Move and Splicing Image Forgery Detection using CNN

“…The paper [2] proposes a system architecture based on ResNet50v2 as basic convolutional model with five stages initialised with YOLO CNN weights for the purpose of object detection and transfer learning. In this paper, it first performs batch normalisation and then it applies activation function to update and optimise the weights.…”

“…Three of the most common manipulations in literature are: (1) Copy-move forgery, in which a specific region from the image is copy pasted within the same image. (2) Removal, in which an image region is removed and the removed part is then in-painted. (3) Splicing, in which a region from an authentic image is copied into a different image.…”