Deepfakes

Deepfake

Synthetic media in which a person's likeness, face, or voice is realistically replaced or manipulated using AI.

GAN (Generative Adversarial Network)

An AI architecture where two networks (generator and discriminator) compete to create realistic synthetic data.

Voice Cloning

Using machine learning models trained on audio samples to synthesise a person's exact vocal tone and cadence.

Digital Provenance

Verifiable records tracing the origin, edits, and authenticity history of digital content.

Follow the investigative process digital forensics experts use to identify synthetic media.

1. 1Facial Boundary Inspection: Look for unnatural blurring, mismatched skin tones, or jittery blending around the jawline and hairline.
2. 2Eye and Blink Analysis: Examine whether blinking patterns look natural and check if lighting reflections in both pupils match the scene.
3. 3Audio-Visual Synchronization: Check if lip movements match phonemes accurately, particularly for plosive sounds like P, B, and M.
4. 4Temporal Consistency: Watch the video frame-by-frame or in slow motion to spot sudden flickering, warping, or disappearing accessories.
5. 5Metadata and Provenance Check: Inspect cryptographic metadata and Content Credentials (C2PA) to confirm camera origin or digital alterations.

python# Conceptual illustration of the Generative Adversarial Network loop
def train_gan(real_sample):
    generator_output = 'synthetic_face_v1.png'
    discriminator_verdict = 'Fake (Confidence: 94%)'
    
    # The generator learns from this rejection to improve realism
    print('Generator output:', generator_output)
    print('Discriminator decision:', discriminator_verdict)
    print('Feedback: Generator adjusts weights to fool discriminator.')

train_gan('real_photo.png')

GANs work as a duel: the generator fabricates candidate media while the discriminator scores its realism, forcing the generator to create increasingly authentic fakes.