Image Origin Classification Based on Social Network Provenance

Caldelli, Roberto; Becarelli, Rudy; Amerini, Irene

doi:10.1109/tifs.2017.2656842

Cited by 69 publications

(60 citation statements)

References 40 publications

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“…The huge amount of images have been uploaded on social networks, and generally stored in a compressed form as JPEG images, after being re-compressed with different compression parameters from those of uploaded images [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Robust Image Identification without Visible Information for JPEG Images

Iida

Kiya

2018

IEICE Trans. Inf. & Syst.

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Kenta IIDA† , Student Member and Hitoshi KIYA †a) , Fellow SUMMARY A robust identification scheme for JPEG images is proposed in this paper. The aim is to robustly identify JPEG images that are generated from the same original image, under various compression conditions such as differences in compression ratios and initial quantization matrices. The proposed scheme does not provide any false negative matches in principle. In addition, secure features, which do not have any visual information, are used to achieve not only a robust identification scheme but also secure one. Conventional schemes can not avoid providing false negative matches under some compression conditions, and are required to manage a secret key for secure identification. The proposed scheme is applicable to the uploading process of images on social networks like Twitter for image retrieval and forensics. A number of experiments are carried out to demonstrate that the effectiveness of the proposed method. The proposed method outperforms conventional ones in terms of query performances, while keeping a reasonable security level. key words: image identification, JPEG, social networks IntroductionThe growth of popularity of social networks(SNs) like twitter and Facebook have opened new perspectives in many research fields, including the emerging area of Multimedia Forensics. The huge amount of images have been uploaded on social networks, and generally stored in a compressed form as JPEG images, after being re-compressed with different compression parameters from those of uploaded im-This paper proposes a new robust identification scheme for JPEG images that are generated from the same original image, under various compression parameters, aiming at producing evidence on the integrity of images e.g. tamper detection. The proposed scheme allows not only to identify JPEG images under the use of different initial quantization matrices and quality factors but also to provide no visible information.So far, several identification schemes and image hash functions have been developed for compressed images [6]- [20]. They can be broadly classified into two types according to a difference in extracted features: compression method-depended type, to which the proposed scheme corresponds, and compression method-independent type. not only are robust against a difference in compressed ratios but also do not produce false negative matches in various compression ratios, due to the use of the positive and negative signs of DCT coefficients. However, they need to be combined with a security technique with a secret key such as the fuzzy commitment scheme [14]- [16], to securely protect the features which have some visible information. In addition, the security technique is not useful for the robust identification when various initial quantization matrices and quality factors are used.Due to such situations, this paper proposes a robust scheme for identifying images compressed under the various compression parameters. The strategy to robustly identify JPEG images is to ...

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Section: Introductionmentioning

confidence: 99%

Robust Image Identification without Visible Information for JPEG Images

Iida

Kiya

2018

IEICE Trans. Inf. & Syst.

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“…Image forensic scientific community has to find image history to classify an image based on through which social network it has been uploaded [1], [2] and [3]. Understanding information about the image could be beneficial for the classification, it includes knowing the acquisition device [4], [5], [6], [7], the device model [8], [9], [10], [11], [12].…”

Section: Related Workmentioning

confidence: 99%

Forensic Technique for Social Network Provenance Classification of Images

Dhanyaja¹

2018

IJRASET

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The unprecedented popularity of various online social networks (OSNs) makes a rapid development to share digital contents online. However, misusing and dissemination of online contents widely happens. Under this circumstance, the identification of the origin and the propagation path of an online image are crucial for many forensic applications. It is a tough task to trace out background details and pre processing of digital image by forensic scientists. Strategic way to address a problem is to simply find image's history: knowing acquisition device and model of camera etc. This paper enlightens image classification based on originating social network. Since image has distinctive traces by social network. Manipulation process will be unique for each social network. Social network provenance based image classification is done through resorting at a trained multi-SVM classifier. Experimental results administrated are distinction attainable on numerous image datasets and in varied operative conditions. Additionally, technique is to go back to the initial JPEG quality image had before being uploaded on a social network.

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“…Although the problem of social network identification was only brought to the attention of multimedia forensic community recently, its importance has led to the introduction of new benchmarking datasets [7], [8], [9]. In [10], a preliminary work has proved that the process to upload images onto Facebook does leave unique and detectable traces in the content.…”

Section: Introductionmentioning

confidence: 99%

“…The same authors later refined their idea in order to classify, using a K-NN classifier, different social networks based on the traces of resizing, compression, renaming and metadata alterations left during the upload/download procedure [9]. Furthermore, in [8] and [11], methods to differentiate social networks such as Facebook, Flickr and Twitter are exploited by adopting only content-based information recovered from DCT (Discrete Cosine Transform) histograms of JPEG images. In [8], social network identification is achieved by means of a Bagged Decision Tree Classifier (BDTC), while a CNN is used to perform classification in [11].…”

Section: Introductionmentioning

confidence: 99%

PRNU-based Image Classification of Origin Social Network with CNN

Caldelli

Amerini

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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A huge amount of images are continuously shared on social networks (SNs) daily and, in most of cases, it is very difficult to reliably establish the SN of provenance of an image when it is recovered from a hard disk, a SD card or a smartphone memory. During an investigation, it could be crucial to be able to distinguish images coming directly from a photo-camera with respect to those downloaded from a social network and possibly, in this last circumstance, determining which is the SN among a defined group. It is well known that each SN leaves peculiar traces on each content during the upload-download process; such traces can be exploited to make image classification. In this work, the idea is to use the PRNU, embedded in every acquired images, as the "carrier" of the particular SN traces which diversely modulate the PRNU. We demonstrate, in this paper, that SNmodulated noise residual can be adopted as a feature to detect the social network of origin by means of a trained convolutional neural network (CNN).

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Image Origin Classification Based on Social Network Provenance

Cited by 69 publications

References 40 publications

Robust Image Identification without Visible Information for JPEG Images

Robust Image Identification without Visible Information for JPEG Images

Forensic Technique for Social Network Provenance Classification of Images

PRNU-based Image Classification of Origin Social Network with CNN

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