A study of co-occurrence based local features for camera model identification

Marra, Francesco; Poggi, Gianfranco; Sansone, Carlo; Verdoliva, Luisa

doi:10.1007/s11042-016-3663-0

Cited by 44 publications

(18 citation statements)

References 46 publications

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“…These methods guarantee very accurate results, especially on full-resolution images that provide sufficient pixel statistics. All these existing techniques are often designed by local parametric models of image data [26,10] or use hand-crafted features [23].…”

Section: Related Workmentioning

confidence: 99%

“…In the last few years, many researchers showed a growing interest in image manipulation detection by applying different computer vision and deep-learning algorithms [29,23,7,8,5,1]. In 2016, Bayar et al [3] used the CNN and developed a new form of convolutional layer that is specifically designed to learn the manipulated features from an image.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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MultiMedia Modeling

2019

Lecture Notes in Computer Science

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The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

MultiMedia Modeling

2019

Lecture Notes in Computer Science

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“…More recent data-driven forensic approaches have leveraged techniques from steganalysis research that capture local pixel dependencies using high dimensional feature sets [9,20]. These approaches have been used to detect image editing [23] and perform camera model identification [4,19]. While these techniques have shown significant improvements in manipulation detection or camera model identificaiton accuracy, researchers are still left with questions such as: Are these the best set of classification features for forensic tasks?…”

Section: Introductionmentioning

confidence: 99%

Design Principles of Convolutional Neural Networks for Multimedia Forensics

Bayar¹,

Stamm²

2017

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Convolutional neural networks (CNNs) have received significant attention due to their ability to adaptively learn classification features directly from data. While CNNs have helped cause dramatic advances in fields such as object and speech recognition, multimedia forensics is fundamentally different problem compared to other deep learning applications. Little work exists to guide the design of CNN architectures for forensic tasks. Furthermore, it is still unclear which forensic tasks can be performed using CNNs. In this work, we investigate the design of CNNs for multiple multimedia forensic applications. We show that CNNs are capable of performing image manipulation detection as well as camera model identification. Through a series of experiments, we systematically examine the influence of several important CNN design choices for forensic applications, such as the use of a constrained convolutional layer or fixed high-pass filter at the beginning of the CNN, the use of nonlinearity after the first layer, the choice of activation and pooling functions, etc. We show that different CNN design choices should be made for different forensic applications and identify design choices to maximize the performance of CNNs for manipulation detection and camera model identification.

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“…Existing image source identification approaches can be divided into three categories, ie, image header based, watermark based, and feature based . The image header based approach relies on studying the image source related information embedded in the image header .…”

Section: Introductionmentioning

confidence: 99%

“…1 As one of the important branches of the cyber security, image source identification has become a hot topic in both academia and industrial communities.Existing image source identification approaches can be divided into three categories, ie, image header based, 2 watermark based, 3,4 and feature based. 5,6 The image header based approach relies on studying the image source related information embedded in the image header. 7 Information related to the image source (such as camera brand, model) is directly embedded in the image header.…”

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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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A study of co-occurrence based local features for camera model identification

Cited by 44 publications

References 46 publications

MultiMedia Modeling

MultiMedia Modeling

Design Principles of Convolutional Neural Networks for Multimedia Forensics

Anti‐noise image source identification

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