@inproceedings{kashyap-etal-2026-revealing,
    title = "Revealing the Truth with {C}on{LLM} for Detecting Multi-Modal Deepfakes",
    author = "Kashyap, Gautam Siddharth  and
      Joshi, Harsh  and
      Jain, Niharika  and
      Shabbir, Ebad  and
      Gao, Jiechao  and
      Joshi, Nipun  and
      Naseem, Usman",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.102/",
    pages = "1968--1978",
    ISBN = "979-8-89176-386-9",
    abstract = "The rapid rise of deepfake technology poses a severe threat to social and political stability by enabling hyper-realistic synthetic media capable of manipulating public perception. However, existing detection methods struggle with two core limitations: (1) modality fragmentation, which leads to poor generalization across diverse and adversarial deepfake modalities; and (2) shallow inter-modal reasoning, resulting in limited detection of fine-grained semantic inconsistencies. To address these, we propose ConLLM (Contrastive Learning with Large Language Models), a hybrid framework for robust multimodal deepfake detection. ConLLM employs a two-stage architecture: stage 1 uses Pre-Trained Models (PTMs) to extract modality-specific embeddings; stage 2 aligns these embeddings via contrastive learning to mitigate modality fragmentation, and refines them using LLM-based reasoning to address shallow inter-modal reasoning by capturing semantic inconsistencies. ConLLM demonstrates strong performance across audio, video, and audio-visual modalities. It reduces audio deepfake EER by up to 50{\%}, improves video accuracy by up to 8{\%}, and achieves approximately 9{\%} accuracy gains in audio-visual tasks. Ablation studies confirm that PTM-based embeddings contribute 9{\%}{--}10{\%} consistent improvements across modalities. Our code and data is available at: https://github.com/gskgautam/ConLLM/tree/main"
}