Artificial Intelligence, Deepfakes and a Future of Ectypes

By Luciano Floridi. 2018

146 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Safety Research, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Russ Howes
2. Saeid Nahavandi
3. Ben Pflaum
4. Mika Westerlund
5. Brian Dolhansky

In Safety Research

1. Russ Howes
2. Ben Pflaum
3. Mika Westerlund
4. Brian Dolhansky

In Human-Computer Interaction

1. Russ Howes
2. Ben Pflaum
3. Mika Westerlund
4. Brian Dolhansky

Citations in the corpus

• The DeepFake Detection Challenge Dataset (2020, background): “The combination of these factors has lead to an explosion in their popularity, both in terms of producing parody videos for entertainment, and for use in targeted attacks against individuals or institutions [9].”
• Deep learning for deepfakes creation and detection: A survey (2019, background): “The inspiration is that what AI has broken can be fixed by AI as well [168].”
• Deep Learning for Deepfakes Creation and Detection (2019, background): “The inspiration is that what AI has broken can be fixed by AI as well [101].”
• MISIM: An End-to-End Neural Code Similarity System (2020, background): “For example, by the phrase “first-degree negative impact” we will refer to a scenario where a given research idea can be directly used for harm (e.g., DeepFake [18], DeepNude 5 , so on).”
• The Emergence of Deepfake Technology: A Review (2019, background): “Again, these results are in line with previous research (Anwar et al., 2019; Atanasova et al., 2019; Bates, 2018; Chawla, 2019; Floridi, 2018; Hasan & Salah, 2019; Qayyum et al., 2018; Spivak, 2019).”
• The DeepFake Detection Challenge (DFDC) Dataset. (2020, unknown): “The combination of these factors has lead to an explosion in their popularity, both in terms of producing parody videos for entertainment, and for use in targeted attacks against individuals or institutions [9].”
• The Deepfake Detection Challenge (DFDC) Preview Dataset (2019, background): “While there are benign and even humorous applications of deepfake videos, when created with malicious intent they have the potential to intimidate and harass individuals [1], or propagate disinformation that can adversely impact elections or financial systems [2, 3].”