Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

Splicing Image and Its Localization:A Survey

With the rapid development of information technology,digital images have become an important medium for information transmission.However,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal.Images are easily spliced and distributed,and the situation will be a great threat to social security.The survey covers splicing image and its localization.The present status of splicing image localization approaches is discussed along with a recommendation for future research.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier.With image processing software at one’s fingertips,one can easily alter the content of an image,and the altered image is so realistic that it is illegible to the naked eye.These tampered images have posed a serious threat to personal privacy,social order,and national security.Therefore,detecting and locating tampered areas in images has important practical significance,and has become an important research topic in the field of multimedia information security.In recent years,deep learning technology has been widely used in image tampering localization,and the achieved performance has significantly surpassed traditional tampering forensics methods.This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning.According to the two types of tampering detection based on deep learning,the detection tasks of the method are detailed separately,and the problems and future prospects in this field are discussed.It is quite different from the existing work:(1)This paper mainly focuses on the problem of image tampering detection,so it does not elaborate on various forensic methods.(2)This paper focuses on the detectionmethod of image tampering based on deep learning.(3)This paper is driven by the needs of tampering targets,so it pays more attention to sorting out methods for different tampering detection tasks.