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Abstract—While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this work, we explore the fundamental limit of operation forensics by proposing an information theoretical framework.

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Abstract—Identifying a signal’s origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal’s acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed. This is an important problem since compressive sensing techniques have seen increased popularity in recent years. In this paper, we propose a set of forensic techniques to identify signals acquired by compressive sensing. We do this by first identifying the fingerprints left in a signal by compressive sensing.

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In recent years, audio and video deepfake technology has advanced relentlessly, severely impacting people's reputation and reliability.
Several factors have facilitated the growing deepfake threat.
On the one hand, the hyper-connected society of social and mass media enables the spread of multimedia content worldwide in real-time, facilitating the dissemination of counterfeit material.

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7 Views

The simplicity and accessibility of tools for generating deepfakes pose a significant technical challenge for their detection and filtering. Many of the recently proposed methods for deeptake detection focus on a `blackbox' approach and therefore suffer from the lack of any additional information about the nature of fake videos beyond the fake or not fake labels. In this paper, we approach deepfake detection by solving the related problem of attribution, where the goal is to distinguish each separate type of a deepfake attack.

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4 Views

There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention.
Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained

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4 Views

There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention.
Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained

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4 Views

Overhead images can be obtained using different acquisition and processing techniques, and they are becoming more and more popular. As with common photographs, they can be forged and manipulated by malicious users. However, not all image forensics methods tailored to normal photos can be successfully applied out of the box to overhead images. In this paper we consider the problem of localizing copy-paste forgeries on panchromatic images acquired with different satellites.

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7 Views

Recently almost all the mainstream deepfake detection methods use Convolutional Neural Networks (CNN) as their backbone. However, due to the overreliance on local texture information, which is usually determined by forgery methods of training data, these CNN based methods cannot generalize well to unseen data. To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images.

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Audio signals are often stored and transmitted in compressed formats. Among the many available audio compression schemes, MPEG-1 Audio Layer III (MP3) is very popular and widely used. Since MP3 is lossy it leaves characteristic traces in the compressed audio which can be used forensically to expose the past history of an audio file. In this paper, we consider the scenario of audio signal manipulation done by temporal splicing of compressed and uncompressed audio signals. We propose a method to find the temporal location of the splices based on transformer networks.

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8 Views

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering.

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