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Deepfakes & multimodalDetection methods
Multimodal Emergent Fake News Detection via Meta Neural Process NetworksExisting fake news detectors trained on labeled data perform poorly on novel, emergent events due to domain shift. The paper proposes MetaFEND, which combines meta-learning and neural processes to leverage a handful of labeled posts from a new event to quickly adapt a detection model.
LLMs & generative AIDeepfakes & multimodal
On the Opportunities and Risks of Foundation ModelsA comprehensive 214-page report analyzing foundation models—large neural networks trained on broad data that can be adapted to diverse downstream tasks (BERT, GPT-3, DALL-E). The report maps capabilities across language, vision, and reasoning; applications in healthcare, law, and education; and societal risks including misuse (generating…
Deepfakes & multimodalDetection methods
A Survey on Multimodal Disinformation DetectionComprehensive survey of multimodal disinformation detection covering text, images, speech, video, network structure, and temporal information. Distinguishes factuality (whether content is false) from harmfulness (whether false content intends to harm), systematically reviews ~140 papers across different modalities and detection…
Detection methodsDeepfakes & multimodal
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal DataMost fake news detection models are trained on a single domain (e.g., politics) and fail when tested on others (e.g., entertainment, COVID-19). This paper proposes a multimodal framework that leverages both domain-specific and cross-domain knowledge to detect fake news across different domains.