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Detecting deepfakes in the presence of code-switching

MBZUAI · Invalid Date

MBZUAI researchers, in collaboration with Monash University, have introduced ArEnAV, a new dataset for deepfake detection featuring Arabic-English code-switching. The dataset comprises 765 hours of manipulated YouTube videos, incorporating intra-utterance code-switching and dialect variations. Experiments showed that code-switching significantly reduces the performance of existing deepfake detectors. Why it matters: This work addresses a critical gap in AI's ability to handle linguistic diversity, particularly in regions where code-switching is prevalent, enhancing the reliability of deepfake detection in real-world scenarios.

SDAIA issues deepfakes guidelines to regulate responsible AI use - Arab News

SDAIA · Jun 11

The Saudi Data and AI Authority (SDAIA) has issued new guidelines specifically designed to address deepfakes. These guidelines aim to regulate the responsible use of AI technologies within the Kingdom. The initiative underscores Saudi Arabia's commitment to establishing a robust framework for ethical AI deployment. Why it matters: This proactive step positions Saudi Arabia as a leading nation in the Middle East in confronting the complex ethical and societal challenges posed by generative AI technologies.