Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security

By R. Chesney, D. Citron. 2018

972 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c20119f9772907a7dc74cf59deae610af885f3ac

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In Sociology and Political Science

Citations in the corpus

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward (2021, background): “As social platforms are largely independent and ad-driven mediums, spreading fabricated information may purely be a profit-making strategy [32].”
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics (2019, background): “Since faces are intrinsically associated with identity, well-crafted DeepFakes can create illusions of a person’s presence and activities that do not occur in reality, which can lead to serious political, social, ﬁnan-cial, and legal consequences [11].”
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues (2020, background): “deepfakes [22], that use artificial intelligence to present false media as realistic in highly sophisticated and convincing ways.”
Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion (2021, background): “This may inspire future work on detecting deep fake images [94, 95].”
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era (2022, background): “The most poignant of those issues is the rise of “deep fakes” (AI-fabricated videos of people saying or doing something that they have never said or done in real life) [181].”
Deepfake Video Detection Using Convolutional Vision Transformer (2021, background): “Technologies for altering images, videos, and audios are developing rapidly [12, 62].”
Deep Learning for Deepfakes Creation and Detection (2019, background): “Deepfakes are increasingly detrimental to privacy, society security and democracy [35].”
Deep learning for deepfakes creation and detection: A survey (2019, background): “Deepfakes are increasingly detrimental to privacy, society security and democracy [71].”
Celeb-DF: A New Dataset for DeepFake Forensics (2019, background): “Since faces are intrinsically associated with an individual’s identity, well-crafted DeepFakes can create illusions of a person’s presence and activities that do not occur in reality, which thereby can lead to serious political, social, financial and legal consequences [5].”
Exposing Deep Fakes Using Inconsistent Head Poses (2018, background): “Together with other forms of misinformation shared through the digital social network, Deep Fakes created digital impersonations have become a serious problem with negative social impacts [8].”
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials (2022, background): “As a result, there is a growing threat of “weaponizating” deepfakes for malicious purposes, which is potentially detrimental to privacy, society security and democracy [12].”
The Creation and Detection of Deepfakes (2020, background): “Although human image editing and synthesis are active research topics, reenactment and replacement deepfakes are the greatest concern because they give an aacker control over one’s identity[12, 28, 66].”