EWJ August 62 2025 web - Journal - Page 17
Digital Forensics:
Tackling the Deepfake Dilemma
In today’s digital age, deepfakes represent a significant challenge for digital forensics experts.
Leveraging sophisticated artificial intelligence (AI), these manipulations create highly convincing yet false content across visual and audio media. In this article Ryan Shields shares how
the S-RM Digital Forensics team are researching and developing forensic methods to enable
detection of these fabricat
distort the visual artefacts and alter audio signals that
might otherwise indicate manipulation.
Technical challenges in deepfake detection
Early deepfakes exhibited indicators such as
mismatched lip-syncing or irregular facial features
which made them easier to detect. However, modern
deepfakes successfully eliminate these issues and
therefore require more advanced detection methods.
Current challenges include:
Addressing the challenges
Each of these challenges requires a concerted effort
involving technology development, continuous research, and the refinement of detection methodologies to effectively combat the risks associated with
deepfakes, with the objective of improving detection
rates. S-RM are developing a dual approach utilising
AI-driven detection tools in combination with the
application of traditional digital forensic analysis.
l Realism and detail: Modern deepfakes utilise
complex algorithms to craft content that is designed to
evade traditional detection, whereby advances in machine learning allow deepfakes to achieve high levels
of realism. As a result, they can closely mimic natural
human expressions, micro-expressions, and subtle
speech nuances, making them difficult to detect with
the naked eye or through simple automated methods.
1. AI-driven detection tools
AI-driven detection tools are designed to identify
subtle irregularities within media content. AI models
are trained on extensive datasets comprised of both
authentic and fabricated media. This training helps
the models learn to recognise patterns and anomalies
that distinguish deepfakes from real content. The tools
identify distinct features typical of deepfakes, such as
pixel-level artefacts, inconsistencies in lighting or
shadow, and unnatural facial or body movements.
l Diverse formats: Deepfakes can be video, audio,
image, or even text-based, each requiring distinct detection approaches. Ensuring detection algorithms
can handle this diversity without being overly
specialised is complex.
l Data loss: Compression algorithms can obscure or
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