Provenance Analysis for Instagram Photos

Quan, Yijun; Lin, Xufeng; Li, Chang-Tsun

doi:10.1007/978-981-13-6661-1_29

Cited by 5 publications

(9 citation statements)

References 20 publications

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“…While these tools enrich the user experience, they may also manipulate the images in a way that may make the SPN-based provenance analysis method ineffective. As a preliminary investigation in [25], we found some image filters of Instagram may cause significant performance drop of existing SPN-based SOC methods. In this work, we will further investigate the effects of Instagram filters and propose a new method to mitigate the impact of image filtering.…”

Section: Background and Related Workmentioning

confidence: 90%

“…We are particularly interested in the effects of image filters on Instagram as it is one of the most popular photo-sharing platforms. Our recent work in [25] has preliminarily shown that some Instagram image filters are particularly harmful to SPN-based clustering. In this work, we conduct further investigation on images from Instagram to gain a better understanding of how SPN-based clustering methods are affected by these filters.…”

Section: Introductionmentioning

confidence: 99%

“…Example images of the 17 Instagram filters together with the original image (Normal) used in our experiment[25].…”

mentioning

confidence: 99%

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Provenance Inference for Instagram Photos Through Device Fingerprinting

Quan

Lin

2020

IEEE Access

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Sensor pattern noise (SPN) has been extensively studied in the scientific community and has found its applications in many practical scenarios in the law-enforcement sector. However, the emergence of photosharing social networking sites (SNSs) poses new challenges to SPN-based digital image provenance analysis. One particular issue is that the SNSs' built-in image editing tools tend to inflict distortion on SPNs. One well-known example of such tools is the image filters on Instagram. We observed that some Instagram image filters manipulate the high-frequency bands of the images and hence damage the SPNs, making source-oriented clustering (SOC) of the filtered images unsatisfactory. To address this issue, we propose to first separate the images processed by different filters beforehand into two groups, with Group Malignant (M) containing the filters that significantly distort SPNs and Group Benign (B) covering the other filters that have no significant impact on SPNs. We then cluster the images processed by Group B filters and calculate the centroid of each cluster, with one centroid representing the reference SPN of the corresponding camera. Finally, we use the centroid of each cluster to attract the images processed by the Group M filters in order to complete the SOC task. To identify the filter applied to each image so as to facilitate the clustering, a convolutional neural network based filter-oriented image classifier is proposed. Tested on 19,332 images processed by 18 different filters, the classifier delivers a very promising accuracy of 98.5%. Moreover, compared to the F1-measure of 47.74% by directly clustering on 1,800 filtered images, our proposed clustering framework achieves a much higher F1-measure of 90.33%. INDEX TERMS sensor pattern noise, image clustering, social network, digital forensics, provenance analysis.

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Section: Background and Related Workmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Provenance Inference for Instagram Photos Through Device Fingerprinting

Quan

Lin

2020

IEEE Access

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“…A novel method for PRNU fingerprint estimation is presented in [9] taking into account the effects of video compression on the PRNU noise through the selection of blocks of frames with at least one non-null DCT coefficient. In [10], a VGG network is employed as classifier to detect the images according to the Insta- gram filter applied, aiming at excluding certain images in the estimation of the PRNU and thus improving the reliability of the device identification method for Instagram photos. The VISION dataset [15] is employed by many methods reviewed in this Section.…”

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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“…The social media identification problem has been widely studied for image files with promising results [3,7,8], employing machine learning classifiers. Recently, Quan et al [9] showed that by using convolutional methods it is possible to recognize Instagram filters and attenuate the sensor pattern noise signal in images. Amerini et al [10] introduced a CNN for learning distinctive features among social networks from the histogram of the discrete cosine transform (DCT) coefficients and the noise residual of the images.…”

Section: Introductionmentioning

confidence: 99%

Learning Double-Compression Video Fingerprints Left From Social-Media Platforms

Amerini

Anagnostopoulos

Maiano

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.

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Provenance Analysis for Instagram Photos

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 5 publications

References 20 publications

Provenance Inference for Instagram Photos Through Device Fingerprinting

Provenance Inference for Instagram Photos Through Device Fingerprinting

Media forensics on social media platforms: a survey

Learning Double-Compression Video Fingerprints Left From Social-Media Platforms

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