Digital Image Forensics Technique for Copy-Move Forgery Detection Using DoG and ORB

Niyishaka, Patrick; Bhagvati, Chakravarthy

doi:10.1007/978-3-030-00692-1_41

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…DoG and ORB are two widely used techniques to automatically detect copy–move manipulation in photos. This method was suggested by Niyishaka et al [ 32 ] and comprises three steps: corners detection with Sobel algorithm [ 33 ]; features extraction with DoG and ORB [ 32 , 34 ]; and, finally, features correspondence. This method combines detection techniques based on blocks and key points in a single model.…”

Section: Literature Review and State Of The Artmentioning

confidence: 99%

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

2021

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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 99.53%, 79.55%, and 89.10%, 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.

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Section: Literature Review and State Of The Artmentioning

confidence: 99%

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

2021

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“…The corresponding mean scores obtained with the 10-fold crossvalidation are also indicated. The results obtained show a mean F1-score above 99.52%, which outperforms the state-of-the-art documented work [2]. The mean value obtained for accuracy (A) is 99.52%, which surpasses the result of 93.52% achieved in [2].…”

Section: Technical Validationmentioning

confidence: 56%

“…The results obtained show a mean F1-score above 99.52%, which outperforms the state-of-the-art documented work [2]. The mean value obtained for accuracy (A) is 99.52%, which surpasses the result of 93.52% achieved in [2]. The number of incorrectly classified examples, namely FP and FN, is low, having a mean value of 7 and 12, respectively.…”

Section: Technical Validationmentioning

confidence: 65%

“…The detection of manipulated multimedia content has gained enthusiasts, especially in the digital forensics context, as the most recurrent today's crimes resort to tampered photos and videos. The Difference of Gaussians (DoG) and Oriented Rotated Brief (ORB) are techniques used to detect copy-move in manipulated photos [2]. DoG applies corners detection with the Sobel algorithm, features extraction with DoG and ORB, and features correspondence.…”

Section: Background and Summarymentioning

confidence: 99%

“…Several works have already processed a compound dataset [3], namely by compiling photos available in CelebA-HQ dataset [11], Flickr-Faces-HQ dataset [12], "100K Faces project" (https://generated.photos, accessed on 4 August 2021) and "this person does not exist" project (https://thispersondoesnotexist.com, accessed on 4 August 2021). The datasets described on Table 1 were tested and benchmarked individually in a wide set of published research works [2][3][4]8]. Notwithstanding the richness of the published datasets, some of them only have deepfake-based manipulations examples.…”

Section: Data Descriptionmentioning

confidence: 99%

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A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Ferreira

Antunes

Correia

2021

Data

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Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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