Source camera identification based on interpolation via lens distortion correction

Hwang, Min Gu; Park, Hyung Ju; Har, Dong Hwan

doi:10.1080/00450618.2013.804946

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…As we know, cameras from different manufacturers have differences in lens distortion parameters, such that the interpolation map for specific camera lens distortion can be considered as fixed. Therefore, Hwang et al [20] used interpolation based lens distortion parameters as a feature to classify the model of camera.…”

Section: Related Workmentioning

confidence: 99%

Source Camera Identification Based on Coupling Coding and Adaptive Filter

et al. 2020

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Source Camera Identification (SCI) has been playing an important role in the security field for decades. With the development of Deep Learning, the performance of SCI has been noteworthily improved. However, most of the proposed methods are forensic only for a single camera identification category, e.g., the camera model identification. For exploiting the coupling between different camera categories, we present a new coding method. That is, we apply the multi-task training method to regress the categories, namely, to classify brands, models and devices synchronously in a single network. Different from the common multi-task method, we obtain the multi-class classification result by just one single label classification. To be specific, we classify the categories in a progressive way that the parent category classification result will be used in the child category classification (a detailed explanation will be given later in the main context). Also, by appropriately increasing the redundancy of the coding method for classifying new camera categories, the training time can be greatly reduced. To better extract camera attributes, we propose an adaptive filter. Additionally, we propose an auxiliary classifier that only focuses on the camera model re-classification, due to the low performance of the main classifier on certain models. Lastly, the extensive experiments show that our methods have a better performance than other existing methods. INDEX TERMS Source camera identification, deep learning, multi-task training, camera categories coupling coding, adaptive filter, auxiliary classifier.

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Section: Related Workmentioning

confidence: 99%

Source Camera Identification Based on Coupling Coding and Adaptive Filter

et al. 2020

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“…The main features in image source identification can be divided into two categories, ie, hardware‐related features and software‐related fingerprint features. Hardware‐related features include pattern noise, lens radial distortion, chromatic aberration, and sensor dust . Software‐related fingerprints include image‐related features and characters of color filter array …”

Section: Related Workmentioning

confidence: 99%

Anti‐noise image source identification

Liu

Huang

Zhang

et al. 2018

Concurrency and Computation

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Summary Image source identification plays an important role in image forensics. Traditional image source identification techniques rely on the assumption that the training datasets are well labeled. However, in the real world, this assumption may not be the truth, and some noisy samples may exist in the training datasets. In this paper, firstly, we theoretically investigate the influence of noisy samples to the identification performance. Then, a new image source identification approach, namely, Anti‐noise Image Source Identification (AISI), is proposed to deal with those noisy samples. AISI has three steps, ie, noise level evaluation, noise level based sampling, and multi‐classification. Noise level evaluation aims to assign each sample with a noise level that indicates the probability of being noisy. The basic idea of noise level based sampling is to sample images according to their noise levels. We provide a theoretically justification to demonstrate the effectiveness of AISI. Experiments conducted on a real‐world image collection confirm that the proposed AISI can alleviate the influence of noisy samples and can improve the identification accuracy while noise exists.

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“…In examining the differences in lens distortion parameters of different brands or models of cameras, Hwang et al proposed a source camera identification method based on the lens distortion correction interpolation attribute. Sensor pattern noise (SPN) is the most serious sensor artifact [9]. It consists of two main parts: fixed pattern noise (FPN) and photo response non-uniformity (PRNU).…”

Section: Introductionmentioning

confidence: 99%

Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

et al. 2021

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In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

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Source camera identification based on interpolation via lens distortion correction

Cited by 8 publications

References 3 publications

Source Camera Identification Based on Coupling Coding and Adaptive Filter

Source Camera Identification Based on Coupling Coding and Adaptive Filter

Anti‐noise image source identification

Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

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