AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.

Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.