Deepfake detection is the task of classifying whether a piece of media — video, audio, or image — has been synthetically generated or manipulated to impersonate a real person. Detection systems are themselves Neural Networks trained on datasets of paired real and fake samples; they learn artifacts such as temporal inconsistencies between frames, subtle blending boundaries around swapped faces, unnatural eye-blink patterns, or spectral anomalies in cloned audio.

Think of it like a forensic document examiner who has studied thousands of forged signatures. The examiner cannot articulate every rule explicitly but has internalized enough statistical regularities to spot deviations that a casual observer would miss.

A binary classifier fine-tuned on a face-forensics dataset might look like this:

from transformers import ViTForImageClassification

model = ViTForImageClassification.from_pretrained(
    "google/vit-base-patch16-224",
    num_labels=2,
    id2label={0: "real", 1: "fake"},
)
# Fine-tune on FaceForensics++ or DFDC dataset

Real-world detection pipelines often analyze sequences of frames rather than single images to catch temporal artifacts that single-frame methods miss.

Deepfake detection is in an adversarial arms race with GAN and Diffusion generation methods: each improvement in synthesis quality degrades detection accuracy, spurring a new round of detector development. The problem is related to Hallucination in the sense that both concern AI outputs that appear plausible but misrepresent reality. Multimodal detectors that jointly analyze audio and video outperform single-modality ones.

You will encounter deepfake detection in content moderation systems, journalism verification tools, and research benchmarks like FaceForensics++ and the Deepfake Detection Challenge dataset.