Abstract
Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of , , and , respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.
Highlights
Cybercrime has challenged national security systems all over the world, and, in the last five years, there has been an increase of 67% in the incidence of security breaches worldwide [1], with malicious activities like phishing, ransomware, and cryptojacking being the most popular threats to cybersecurity [2,3,4]
An Support Vector Machines (SVM)-based method was implemented in a standalone application, to process the previously extracted features obtained by a Discrete Fourier Transform (DFT) calculation in each multimedia file
The most relevant and up-to-date literature review related to digital forensics on multimedia content was made, namely the survey on deep learning-based methods applied to photos and videos forensics
Summary
Cybercrime has challenged national security systems all over the world, and, in the last five years, there has been an increase of 67% in the incidence of security breaches worldwide [1], with malicious activities like phishing, ransomware, and cryptojacking being the most popular threats to cybersecurity [2,3,4]. SVM-based model [20] to detect discrepancies in photos and video frames, namely splicing and copy–move anomalies It works by extracting a set of fifty features calculated by a Discrete Fourier Transform (DFT), applied to the input files that are further processed by an SVM-based method. These Autopsy modules were tested with a classified dataset of about 40,000 photos and 800 videos, composed of both faces and objects, where it is possible to find examples of slicing and copy–move manipulations. The Autopsy modules take advantage of the SVM-based model implemented as a standalone application and have been made available in the following GitHub repository: https://github.com/saraferreirascf/Photo-and-video-manipulations-detector (accessed on 22 June 2021).
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