Deep siamese network for limited labels classification in source camera identification

Sameer, Venkata Udaya; Naskar, Ruchira

doi:10.1007/s11042-020-09106-y

Cited by 19 publications

(16 citation statements)

References 36 publications

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“…Liang et al [18] used the attention multisource fusion few-shot learning method (AMF-FSL) to transfer the classification ability of few-shot learning from multisource data to target data, which improved the generalization ability of the classification model in cross-domain. Sameer and Naskar [19] used the deep Siamese network method to enhance the training space by forming paired samples from the same camera model and different camera models and obtained a better model of camera source identification problem. Huo et al [20] focused on the scene of zero sample and few sample mixed learning with extreme scarcity.…”

Section: Related Workmentioning

confidence: 99%

Multi-DS Strategy for Source Camera Identification in Few-Shot Sample Data Sets

Wang

Hou

et al. 2022

Security and Communication Networks

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Source camera identification (SCI) is an intriguing problem in digital forensics, which identifies the source device of given images. However, most existing works require sufficient training samples to ensure performance. In this work, we propose a method based on semi-supervised ensemble learning (multi-DS) strategy, which extends labeled data set by multi-distance-based clustering strategy and then calibrate the pseudo-labels through a self-correction mechanism. Next, we iteratively perform the calibration-appending-training process to improve our model. We design comprehensive experiments, and our model achieves satisfactory performance on benchmark public databases (i.e., Dresden, VISION, and SOCRatES).

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Section: Related Workmentioning

confidence: 99%

Multi-DS Strategy for Source Camera Identification in Few-Shot Sample Data Sets

Wang

Hou

et al. 2022

Security and Communication Networks

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“…Methods in [ 128 , 129 ] both used Siamese network for this camera classification problem. There are multiple inputs in a Siamese network with the same architecture and same initial weights for each sub-network.…”

Section: Other Specific Forensic Problemsmentioning

confidence: 99%

“…In [ 128 ], authors proposed a Siamese CNN to extract the camera unique fixed-pattern noise from an image’s Photo Response Non-Uniformity (PRNU) to classify camera devices and furthermore trace device fingerprints for image forgery detection. Sameer and Naska [ 129 ] worked on the scenario where annotated data (i.e., in this case image samples) were not available in big quantities and training had to be performed using a limited number of samples per class. This approach is called few-shot learning and refers to learning and understanding a new model based on a few examples.…”

Section: Other Specific Forensic Problemsmentioning

confidence: 99%

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Camacho

Wang

2021

J. Imaging

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Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

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“…Nowadays, the Siamese neural network has become a popular method in image recognition, partly due to the advantage that it does not require a large amount of data in the inference phase [14][15][16][17][18]. The Siamese neural network takes two samples as the input and outputs the spatial features after dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

“…Figueroa-Mata et al [19] proposed the use of a convolution Siamese network (CSN) for image-based plant species recognition on small data sets to distinguish plant species based on leaf images. Sameer et al [18] proposed a deep Siamese network for limited label classification in source cameras. This method has been applied in label classification and recognition, but it has not been studied in face recognition.…”

Section: Introductionmentioning

confidence: 99%

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Sun

et al. 2021

Electronics

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Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

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Deep siamese network for limited labels classification in source camera identification

Cited by 19 publications

References 36 publications

Multi-DS Strategy for Source Camera Identification in Few-Shot Sample Data Sets

Multi-DS Strategy for Source Camera Identification in Few-Shot Sample Data Sets

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

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