Sex, Lies, and Videotape: Deep Fakes and Free Speech Delusions

By M. Franks, A. Waldman. 2019

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

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

In Safety, Robustness & Interpretability

1. Robin Rombach
2. Björn Ommer
3. Patrick Esser
4. Raphael Gontijo Lopes
5. Mohammad Norouzi
6. Kevin P. Murphy
7. Dilip Krishnan
8. Ming-Hsuan Yang

Citations in the corpus

• High-Resolution Image Synthesis with Latent Diffusion Models (2021, background): “In particular, the deliberate manipulation of images (“deep fakes”) is a common problem in this context, and women in particular are disproportionately affected by it [13, 24].”
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (2022, background): “On the other hand, generative methods can be leveraged for malicious purposes, including harassment and misinformation spread [20], and raise many concerns regarding social and cultural exclusion and bias [70, 65, 71].”
• Muse: Text-To-Image Generation via Masked Generative Transformers (2023, background): “However, it is well known that they can also be leveraged for misinformation, harassment and various types of social and cultural biases (Franks & Waldman, 2018; Whittaker et al., 2020; Srinivasan & Uchino, 2021; Steed & Caliskan, 2021).”