Multi-Clue Reconstruction of Sharing Chains for Social Media Images

Verde, Sebastiano; Pasquini, Cecilia; Lago, Federica; Alessandro, Goller,; Natale, Francesco G. B. De; Piva, Alessandro; Boato, Giulia

doi:10.1109/tmm.2023.3253389

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…In order to instantiate the framework described in Section 2, we consider the feature representation proposed in [41], which includes both content-based (namely DCT) and container-based features (namely META and HEADER).…”

Section: B Featuresmentioning

confidence: 99%

Toward Open-World Multimedia Forensics Through Media Signature Encoding

Baracchi,

Boato,

De Natale

et al. 2024

IEEE Access

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Countering image and video manipulations is getting more and more relevant in several fields such as investigation, intelligence and forensics. Multimedia forensics researchers keep developing new tools and updating available detectors to discriminate the processing the media has been subjected to. Even though these tools can be effectively used under controlled environments, they are generally unreliable in open-world settings, where the investigated content may have undergone several unknown processing. In this paper, we present a novel framework to discriminate different toolchains of media manipulation and processing. We introduce the concept of media signature encoding to map image and video contents in spaces where media corresponding to similar processing toolchains cluster together. We also demonstrate that this property still holds for toolchain that are not known when building the encoder, thus making the framework applicable in open-world settings where the forensic practitioner can face both known and unknown manipulations. It is worth to note that an advantage of this approach is that in principle media signatures can be generated starting from any kind of forensic features. We tested the effectiveness of the proposed framework in two different experimental setups involving digital images and videos, respectively. Experiments show that encoded signatures are capable of determining whether: (i) a media under analysis belongs to a known life cycle or an entirely novel processing toolchain; (ii) a subset of media items share the same history. This framework can be considered a first step towards the use of forensic features to characterize media life cycles in open-world settings.INDEX TERMS multimedia forensics, media signature, feature fusion, autoencoders I. INTRODUCTION

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Section: B Featuresmentioning

confidence: 99%

Toward Open-World Multimedia Forensics Through Media Signature Encoding

Baracchi,

Boato,

De Natale

et al. 2024

IEEE Access

Self Cite

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“…With nearly 4 billion active users worldwide, approximately 3.2 billion images are shared each day, and more than 500 h of video uploaded to YouTube every minute 1,2 . This surge of content not only reflects the dynamism of online interactions but presents a unique opportunity for social media forensics in investigations across various domains 3 …”

Section: Introductionmentioning

confidence: 99%

Toward social media forensics through development of iOS analyzers for evidence collection and analysis

Hyder,

Arshad,

Fatima

2024

Concurrency and Computation

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SummarySocial media usage in mobile phones has increased substantially in recent times, and they are a critically important source of a forensics investigation. In this paper, we have developed Python‐based forensic analyzers that are integrated with the open‐source tool Autopsy. The proposed analyzers find forensic artifacts from the three most widely used social media messaging applications, that is, WhatsApp, Instagram, and Facebook Messenger. This research focuses on finding forensic artifacts stored by these social media applications on an iOS device. These analyzers extract data critical for a forensic investigation such as text messages, media attachments, sender and receiver details, timestamps, contact information, and other related forensics data from the full file system image of iOS devices. These Python‐based plugins extract the required data from the social media applications' databases and present the evidential artifacts in a human‐readable format. We integrated these analyzers into the Autopsy Forensics tool and showcased the gathered evidence so that investigators are capable to analyze the extracted information effortlessly. The data integrity is maintained by converting it into readable form without permanently altering the database format. The results prove that the proposed analyzers can successfully extract and analyze forensics data at a low computational overhead.

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“…T ODAY a large number of images is acquired by digital devices, manipulated by editing software and uploaded to Social Networks and Instant Messaging platforms. Multiple JPEG compressions ([1]- [3]) are then usually applied on the considered image and part of the data contained in the original picture is actually lost. One of the most common problems in the image forensics field is then the reconstruction of the history of an image ([4]- [6]).…”

mentioning

confidence: 99%

Exploiting AC Histogram Statistics for Misalignment Estimation in Double JPEG Compressed Images

Battiato,

Guarnera,

Puglisi

2024

IEEE Access

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A critical task in forensics investigation is the recovery of the manipulation history of the image under analysis. To this aim, considering a typical life-cycle of a digital image, the estimation of the misalignment occurred between consecutive JPEG compressions can be considered an useful starting point to localize forgeries and retrieve information about the camera that took the picture through first quantization matrix estimation. In this work, starting from statistics computed from the AC histograms obtained applying a third JPEG compression, an effective and robust deep learning based approach devoted to estimate the aforementioned misalignment has been designed. Finally, to assess the performance of the proposed solution a series of tests has been conducted at varying of patch sizes, quantization matrices, employed datasets, and comparisons with state-of-the-art solutions.INDEX TERMS Image forensics, non-aligned double JPEG compression, misalignment estimation, DCT coefficient analysis.

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Multi-Clue Reconstruction of Sharing Chains for Social Media Images

Cited by 8 publications

References 31 publications

Toward Open-World Multimedia Forensics Through Media Signature Encoding

Toward Open-World Multimedia Forensics Through Media Signature Encoding

Toward social media forensics through development of iOS analyzers for evidence collection and analysis

Exploiting AC Histogram Statistics for Misalignment Estimation in Double JPEG Compressed Images

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