A novel deep learning framework for copy-moveforgery detection in images

Elaskily, Mohamed A.; Elnemr, Heba A.; Sedik, Ahmed; Dessouky, Mohamed M.; Banby, Ghada M. El; Elshakankiry, Osama A.; Khalaf, Ashraf A. M.; Aslan, Heba K.; Faragallah, Osama S.; El‐Samie, Fathi E. Abd

doi:10.1007/s11042-020-08751-7

Cited by 70 publications

(26 citation statements)

References 40 publications

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“…The MICC F220 dataset was used in the experiments, which achieves a precision of 98%, recall of 89.5%, F 1 -score of 92%, and accuracy of 95%. The main contribution of the research in [ 42 ] is the development of a CNN for categorizing images into two groups: authentic and forged. Image features are extracted and feature maps are created by the CNN.…”

Section: Related Workmentioning

confidence: 99%

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Kadam

Ahirrao

Kotecha

2022

Computational Intelligence and Neuroscience

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With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.

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Section: Related Workmentioning

confidence: 99%

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Kadam

Ahirrao

Kotecha

2022

Computational Intelligence and Neuroscience

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“…The single branch CNN architecture designed by Elaskily et al . [34] is deeper and heavier than the proposed model. It uses convolution layers with 16, 32, 64, 128, 256 and 512 feature maps, whereas our model uses only 16, 32 and 64 feature maps in the convolution layers with maximum of 64 feature maps in the last convolution layer in both the branches.…”

Section: Comparative Analysismentioning

confidence: 92%

Dual branch convolutional neural network for copy move forgery detection

Goel

Kaur

Bala

2020

IET Image Processing

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The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning–based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi‐scale features by employing different kernel sizes in each branch. Fusion of extracted multi‐scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F‐2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.

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“…Like LSTM, in ConvLSTM the current state depends on all previous states as depicted in Figure 6 [12] and formulated in Equations ( 2) to (6). Consequently, it can be inferred that if states are viewed as they would in the hidden representations of moving objects, then the larger transitional kernel of a ConvLSTM should be capable of capturing faster motions [12][13][14][15][16][17][18].…”

Section: Proposed Framework and Deep Learning Models (Dlms)mentioning

confidence: 99%

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

et al. 2021

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Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities where intelligent access control is deployed for identity authentication, online banking, and cyber security. The prevalence of biometrics, such as fingerprints, in authentication and identification make the need to safeguard them important across different areas of smart applications. Our study proposes a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we utilise deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a threetier probability that a biometric had been tempered. Simulation-based experiments reported indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in smart cities.

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A novel deep learning framework for copy-moveforgery detection in images

Cited by 70 publications

References 40 publications

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Dual branch convolutional neural network for copy move forgery detection

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

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