We introduce the Microsoft-Northwestern-WITNESS (MNW) deepfake detection benchmark, a dataset designed to evaluate and improve artificial intelligence (AI)-generated content detection algorithms. The dataset contains more than 50,000 artifacts (images, videos, and audio files) generated by us. It also includes real-world examples of AI-manipulated or suspicious media encountered by journalists and human rights defenders globally, annotated by experts to reflect practical, high-stakes detection scenarios. The MNW dataset will be periodically updated to cover emerging generators and includes adversarial examples created with state-of-the-art attacks. This is a collaborative effort, and we encourage generative AI model developers to help maintain the dataset\u2019s currency. This dataset is intended solely for evaluation purposes and cannot be used for training or commercial purposes. We recommend that entities purchasing detection solutions avoid using our dataset to evaluate commercial tools. Our goal is to establish high standards for developers and enhance the reliability of detection systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
We introduce the Microsoft-Northwestern-WITNESS (MNW) deepfake detection benchmark, a dataset designed to evaluate and improve artificial intelligence (AI)-generated content detection algorithms. The dataset contains more than 50,000 artifacts (images, videos, and audio files) generated by us. 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