This paper introduces a novel Deepfake Detection Visual Question Answering (DD-VQA) task to enhance deepfake detection by incorporating human common-sense reasoning. The DD-VQA task involves generating both detection decisions and textual explanations based on questions and images. The authors propose a new annotated dataset and a Vision and Language Transformer-based framework for the DD-VQA task. They also introduce text and image-aware feature alignment to improve multi-modal representation learning. The proposed method enhances existing deepfake detection models by integrating learned vision representations that reason over common-sense knowledge from the DD-VQA task. The authors provide extensive empirical results showing that their method improves detection performance, generalization ability, and language-based interpretability in deepfake detection. The dataset is available at https://github.com/Reality-Defender/Research-DD-VQA. The paper also discusses related works, including deepfake detection, interpretable deepfake detection models, and vision-language learning. The authors present their method, which includes constructing the DD-VQA dataset, designing a multi-modal Transformer model, and training objectives for the DD-VQA task. They evaluate their method on the DD-VQA dataset and existing deepfake detection models, showing that their approach improves detection performance and generalization ability. The paper also includes qualitative examples and ablation studies to demonstrate the effectiveness of their method. The authors conclude that their approach enhances deepfake detection by incorporating human common-sense reasoning and provides a benchmark for future research.This paper introduces a novel Deepfake Detection Visual Question Answering (DD-VQA) task to enhance deepfake detection by incorporating human common-sense reasoning. The DD-VQA task involves generating both detection decisions and textual explanations based on questions and images. The authors propose a new annotated dataset and a Vision and Language Transformer-based framework for the DD-VQA task. They also introduce text and image-aware feature alignment to improve multi-modal representation learning. The proposed method enhances existing deepfake detection models by integrating learned vision representations that reason over common-sense knowledge from the DD-VQA task. The authors provide extensive empirical results showing that their method improves detection performance, generalization ability, and language-based interpretability in deepfake detection. The dataset is available at https://github.com/Reality-Defender/Research-DD-VQA. The paper also discusses related works, including deepfake detection, interpretable deepfake detection models, and vision-language learning. The authors present their method, which includes constructing the DD-VQA dataset, designing a multi-modal Transformer model, and training objectives for the DD-VQA task. They evaluate their method on the DD-VQA dataset and existing deepfake detection models, showing that their approach improves detection performance and generalization ability. The paper also includes qualitative examples and ablation studies to demonstrate the effectiveness of their method. The authors conclude that their approach enhances deepfake detection by incorporating human common-sense reasoning and provides a benchmark for future research.