This dataset contains anonymized responses from 600 Egyptian citizens collected in March 2025 to assess public perceptions of artificial intelligence (AI) and deepfake technologies used in the animation of ancient pharaonic statues and symbols. The survey was conducted as part of a broader research study titled "Animating the Sacred: The Ethical and Cultural Implications of AI-Powered Awakening of Pharaonic Symbols Using Deepfake Techniques."
"Recent advancements in deep learning and generative models have significantly enhanced text-to-image (T2I) synthesis, allowing for the creation of highly realistic images based on textual inputs. While this progress has expanded the creative and practical applications of AI, it also presents new challenges in distinguishing between authentic and AI-generated images. This challenge raises serious concerns in areas such as security, privacy, and digital forensics.
The Deepfake face detection task involves a facial image of unknown authenticity for testing. While most deepfake detection methods take only the image as input, our literature demonstrates that conditioning the deepfake detector on identity—i.e., knowing whose deepfake face the picture might be—can enhance detection performance. Existing deepfake detection datasets, such as FaceForensics++ and DFDC, do not include identity information for authentic and deepfake faces.
