Detecting Deepfakes: Challenges and Solutions

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The chapter explores the complexities of deepfake detection, emphasizing the need for accurate detection methods to uphold trust in content. It discusses the training pipeline, varying detection techniques, and the importance of setting thresholds based on popular platforms. The conversation also highlights the diverse sectors interested in deep fake detection and the broader impact of deep fakes on content consumption and trust.

A deepfake is a synthetic media technique that uses deep learning to create or manipulate video, audio, or images to present something that didn’t actually occur. Deepfakes have gained attention in part due to their potential for misuse, such as creating forged videos for political manipulation or spreading misinformation.

Ryan Ofman is a Lead Engineer and Head of Science Communication at DeepMedia, which is a platform for AI-powered deepfake detection. He joins the show to talk about the state of deepfakes, their origin, and how to detect them.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .