The emergence of contemporary deepfakes has attracted significant attention
in machine learning research, as artificial intelligence (AI) generated
synthetic media increases the incidence of misinterpretation and is difficult
to distinguish from genuine content. Currently, machine learning techniques
have been extensively studied for automatically detecting deepfakes. However,
human perception has been less explored. Malicious deepfakes could ultimately
cause public and social problems. Can we humans correctly perceive the
authenticity of the content of the videos we watch? The answer is obviously
uncertain; therefore, this paper aims to evaluate the human ability to discern
deepfake videos through a subjective study. We present our findings by
comparing human observers to five state-ofthe-art audiovisual deepfake
detection models. To this end, we used gamification concepts to provide 110
participants (55 native English speakers and 55 non-native English speakers)
with a webbased platform where they could access a series of 40 videos (20 real
and 20 fake) to determine their authenticity. Each participant performed the
experiment twice with the same 40 videos in different random orders. The videos
are manually selected from the FakeAVCeleb dataset. We found that all AI models
performed better than humans when evaluated on the same 40 videos. The study
also reveals that while deception is not impossible, humans tend to
overestimate their detection capabilities. Our experimental results may help
benchmark human versus machine performance, advance forensics analysis, and
enable adaptive countermeasures.

人们对深度伪造视频的感知能力进行主观研究，结果显示机器学习模型相对于人类在同样的 40 个视频上有更好的表现，而人们又往往高估了自己的检测能力。这一研究结果有助于对比人类和机器的性能、推进法医分析，并实现自适应对策。

揭示幻觉：理解人类对音视频深度伪造的感知

Unmasking Illusions: Understanding Human Perception of Audiovisual  Deepfakes

Detection of double JPEG compression is important to forensics analysis. A
few methods were proposed based on convolutional neural networks (CNNs). These
methods only accept inputs from pre-processed data, such as histogram features
and/or decompressed images. In this paper, we present a CNN solution by using
raw DCT (discrete cosine transformation) coefficients from JPEG images as
input. Considering the DCT sub-band nature in JPEG, a multiple-branch CNN
structure has been designed to reveal whether a JPEG format image has been
doubly compressed. Comparing with previous methods, the proposed method
provides end-to-end detection capability. Extensive experiments have been
carried out to demonstrate the effectiveness of the proposed network.

本文提出了一种使用 JPEG 图像的 DCT 系数作为输入的 CNN 解决方案来检测双 JPEG 压缩的方法，并且相对于以往方法具备端到端的检测能力。