RemNet: remnant convolutional neural network for camera model identification

Rafi, Abdul Muntakim; Tonmoy, Thamidul Islam; Kamal, Uday; Wu, Q. M. Jonathan; Hasan, Md. Kamrul

doi:10.1007/s00521-020-05220-y

Cited by 24 publications

(26 citation statements)

References 33 publications

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“…Recently, a new dataset for source camera identification is proposed (the Forchheim Image Database -FODB) [16] considering five different social networks. Two CNNs methods have been evaluated [17,18] with and without degradation generated on the images by the sharing operation. An overview of the obtained results is shown in Fig.…”

Section: Device and Model Identification: Perfect Knowledge Methodsmentioning

confidence: 99%

Media forensics on social media platforms: a survey

Pasquini

Amerini

Boato

2021

EURASIP J. on Info. Security

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The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future.

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Section: Device and Model Identification: Perfect Knowledge Methodsmentioning

confidence: 99%

Media forensics on social media platforms: a survey

Pasquini

Amerini

Boato

2021

EURASIP J. on Info. Security

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“…A larger kernel is preferred for information that is distributed more globally, and a smaller kernel is preferred for information that is distributed locally. Therefore, unlike [37], we design a single block that uses a multi-level feature extracting module by using several parallel path convolution blocks inspired by the concept used in [38], [39], [40]. Each convolution block consists of convolution kernels, a batch normalization layer followed by a ReLU activation layer.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…1) Multi-level CNN-based Preprocessor: Inspired by the concept of residual image generation module design of Rem-Net [37], a network shown to be extremely suitable for camera model identification from the hidden fingerprints of the input images, we propose here a dynamic module that enhances the desired features of an input X-ray image by subtracting from it the activations extracted using a module similar to the Inception module [38], [39], [40]. Salient parts in the CXRs can have variation in size and position.…”

Section: Layermentioning

confidence: 99%

ReCoNet: Multi-level Preprocessing of Chest X-rays for COVID-19 Detection Using Convolutional Neural Networks

Ahmed

Yap

Tan

et al. 2020

Preprint

Self Cite

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Life-threatening COVID-19 detection from radiomic features has become a dire need of the present time for infection control and socio-economic crisis management around the world. In this paper, a novel convolutional neural network (CNN) architecture, ReCoNet (residual image-based COVID-19 detection network), is proposed for COVID-19 detection. This is achieved from chest X-ray (CXR) images shedding light on the preprocessing task considered to be very useful for enhancing the COVID-19 fingerprints. The proposed modular architecture consists of a CNN-based multi-level preprocessing filter block in cascade with a multi-layer CNN-based feature extractor and a classification block. A multi-task learning loss function is adopted for optimization of the preprocessing block trained end-to-end with the rest of the proposed network. Additionally, a data augmentation technique is applied for boosting the network performance. The whole network when pre-trained end-to-end on the CheXpert open source dataset, and trained and tested with the COVIDx dataset of 15,134 original CXR images yielded an overall benchmark accuracy, sensitivity, and specificity of 97.48%, 96.39%, and 97.53%, respectively. The immense potential of ReCoNet may be exploited in clinics for rapid and safe detection of COVID-19 globally, in particular in the low and middle income countries where RT-PCR labs and/or kits are in a serious crisis.

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“…In some cases, deep networks are utilized for feature extraction only, whereas camera identification is performed by other classifiers [ 21 , 25 ]. Moreover, there have also been networks designed specifically for source camera identification, such as the richer convolutional feature network-based representation [ 26 ], RemNet [ 27 ], and Siamese network-based works [ 28 , 29 ]. Other than the above works on the fixed data set, Sameer et al studied the problem of blind identification of social networks images [ 30 ], whereas the open-set problem is discussed in [ 31 , 32 ] with shallow networks.…”

Section: Introductionmentioning

confidence: 99%

Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction

Liu

Zou

Yang

et al. 2021

Sensors

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Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

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RemNet: remnant convolutional neural network for camera model identification

Cited by 24 publications

References 33 publications

Media forensics on social media platforms: a survey

Media forensics on social media platforms: a survey

ReCoNet: Multi-level Preprocessing of Chest X-rays for COVID-19 Detection Using Convolutional Neural Networks

Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction

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