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Most of researches on image forensics have been mainly focused on detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms are not perfect, and accordingly the provide...
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... necessary data for the subsequent steps. This data includes features vectors from images for each tool and they are converted to decision values by a Support Vector Machine (SVM). These decision values are used for fusion based on an NFIS in the second step,. Analysis of the results and making final decisions are carried out in the last step. Fig. 1 illustrates the framework for fusion of forgery detection ...
Citations
... We introduced (Polar DyWT) which was more efficient. Similarly, ANFIS [15] identifies the forgery in the case of splicing only, and this classifier still needs to be explored for further research. ...
... The method is invariant to reflection attacks and any combination of reflection attacks with geometrical transforms. In [26], the fusion of forgery detection methods based on Fuzzy inference rules is addressed. A neuro-fuzzy inference system (NFIS) is developed for forgery detection for better time complexity. ...
In today’s modern era, digital images have noteworthy significance because they have become a leading source of information dissemination. However, the images are being manipulated and tampered. The image manipulation is as old as images itself. The history of modifying images dates back to the 1860s’, though it has become very popular in recent times due to the availability of various open source software available freely over the internet. Such software is responsible for eroding our trust on the integrity of the visual imagery. In this paper, a comprehensive survey of various image forgeries, its types and the currently used techniques to detect such forgeries is presented. The review delivers the downsides of various controversial forgeries that have happened in the history. It provides the taxonomy of various forgeries in digital images and a redefined the classification of forgery detection methods. It also highlights the pros and cons of forgery detection methods currently in use and directs path towards challenges for further research.
... The method is invariant to reflection attacks and any combination of reflection attacks with geometrical transforms. In [26], the fusion of forgery detection methods based on Fuzzy inference rules is addressed. A neuro-fuzzy inference system (NFIS) is developed for forgery detection for better time complexity. ...
The authenticity of digital images is openly challenged today due to the easy availability of various advanced image editing software. The semantic meaning of an image can be changed upto any extent with the help of these software. Image splicing forgery is one of the most popular ways to manipulate the content of an image. In image splicing forgery, two or more images or the parts of the images are used to create a spliced (composite) image. Spliced images can be misused in many ways. Therefore, to revive the trustworthiness of digital images, several efforts are made by researchers to develop various methods to detect image splicing forgery in the last few years. The main objective of this study is to review and analyze the recent work in this area. In this paper, first, a generalized workflow to detect image splicing forgery is presented. Second, this paper categorized the existing image splicing detection methods as hand-crafted feature-based and deep learning-based. Third, various publicly available image datasets are also summarized. Finally, future research directions are provided to help the researchers.
