Page 126 - Cyber Defense eMagazine September 2025

and audio used in election cycles can tarnish candidates, misinform voters, and undermine the legitimacy
of democratic processes.

Perhaps most fundamentally, the rise of synthetic media challenges the principle of “seeing is believing.”
As the line between reality and fabrication blurs, the authenticity of digital communications, online
evidence, and documentation becomes questionable, threatening the fabric of digital trust society relies
upon.

Detection Techniques and Their Limitations

AI/ML-based Detectors

Detection of deepfakes typically relies on artificial intelligence and machine learning models trained to
identify anomalies characteristic of synthetic content. These detectors analyze artifacts such as
inconsistencies in facial movements, blinking rates, light reflections, or audio-visual mismatches. Some
algorithms can detect subtle unnatural features left behind by GANs or identify statistical differences
between real and fake samples.

Biometric and Watermarking Solutions

Biometric analysis focuses on physiological traits, like heart rate inferred from facial video or micro-
expressions difficult for GANs to reproduce. Meanwhile, digital watermarking involves embedding
invisible markers or digital signatures in authentic media, which can later be checked to verify integrity.

Crowd-sourced and Manual Verification

Manual verification by trained professionals, journalists, or fact-checkers remains a valuable approach,
especially when automated tools produce inconclusive results. Crowd-sourcing platforms and
community-driven services can rapidly vet viral content, although such efforts are labor-intensive and not
always timely.

Blockchain and Authenticity Tags

Blockchain-based systems and tamper-evident metadata can offer methods for recording and tracing the
provenance of digital media. By establishing audit trails and issuing authenticity tags at the point of
creation, these systems help verify that media has not been altered.

Limitations

No detection method is foolproof. As deepfake generation methods improve, so do their abilities to evade
AI-driven detectors. The ongoing arms race between creators and defenders of synthetic media means
detection algorithms require continuous updating and retraining as new attack methods emerge.
Moreover, detection is further complicated when deepfakes are of low resolution, fleeting in nature (e.g.,
live streams), or specifically tailored to defeat known detection models.

Cyber Defense eMagazine – September 2025 Edition 126
Copyright © 2025, Cyber Defense Magazine. All rights reserved worldwide.