/pdf/benford-s-law-applied-to-digital-forensic-analysis-1r03qc8j.pdf

Benford's law applied to digital forensic analysis

Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the “support vector machine” (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel. When tested on publicly available image dataset “CASIA” v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms. 

Image Splicing Detection Using Generalized Whittaker Function Descriptor

In an age where preserving knowledge and information from books and documents is crucial, traditional manual scanning methods are tedious and error-prone. It involves a lot of human intervention and, as a result, sometimes results in erroneous digitization, which makes the downstream tasks, such as optical character recognition, difficult. Therefore, innovative techniques are required to be proposed that not only reduce human effort in terms of digitization but also give highly accurate results over the recently proposed state-of-the-art techniques. We proposed a novel computer vision-based algorithm that combines Gray-Level Co-occurrence Matrix (GLCM) features with Thepade's 10-ary texture features (TSBTC) for video frame classification. This hybrid approach significantly enhances frame selection accuracy, ensures high-quality digitization, and accommodates multiple languages and document types. We also proposed a dataset of 54,000 diverse images to demonstrate our algorithm's effectiveness in real-world scenarios and compare it to existing methods, making a valuable contribution to document digitization. The proposed dataset can be utilized for several document image analysis tasks. 

Enhancing Book and Document Digitization from Videos: A Feature Fusion-Based Approach

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford’s law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier short

/pdf/a-novel-image-splicing-detection-algorithm-based-on-30ua1qw6.pdf

A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

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A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law