The Arrest Was Real. But Was the Photograph?
Two AFP-released photographs, the same camera-class label, and very different forensic likelihood ratios. What SPOT found in the Kazem Hamad case.
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Complete insights library
All editorial records, newest additions first. Publications added together are ordered by publication year.
Search by question or topic ↗AI-Generated CSAM: When Everything Is a Priority, Nothing Is
Detecting synthetic imagery is only part of the problem. The greater challenge is ensuring investigative resources reach the children who need protecting.
OpenAI just said there may be “no scientific way” to detect AI-generated images. That’s bullshit. Here’s why.
The absence of a universal, infallible detector does not mean image origin is beyond scientific examination. Forensic science works by measuring evidence and defining the limits of an inference.
AI-Generated Images as Evidence: Authentication, Provenance and Forensic Examination
Forensic examination starts with the disputed proposition, the supplied file and the history of any transformations.
Are AI Image Detectors Reliable? Why Accuracy Alone Is Not Enough
Detector suitability depends on error rates, threshold, calibration, prevalence and how closely validation resembles the material being examined.
C2PA and Content Credentials: What Happens When Provenance Metadata Is Missing?
A missing Content Credential is a missing provenance record, not a classification of an image as synthetic.
Can AI-Generated Images Be Reliably Detected? A Forensic Perspective
Image-origin assessment depends on the evidence available, the competing hypotheses and the conditions under which a method was tested.
Deepfake Detection vs Synthetic-Media Forensics: What Can Actually Be Established?
Deepfake is a broad label. A forensic conclusion must identify the medium, alleged alteration and origin proposition actually examined.
How Can You Tell Whether an Image Was AI-Generated?
A practical reading of visual clues, metadata, Content Credentials and forensic measurements when the origin of an image is questioned.
Synthetic-Media Forensics for Investigators: A Practical Framework
An evidential workflow for preserving questioned media, defining origin propositions and reporting uncertainty.
Explainer: how law enforcement decodes your photos
Richard Matthews’ 2017 explanation of image forensics, metadata and camera identification, republished from The Conversation.
Ghost protocol – Snapchat as a method of surveillance
A 2021 study of public Snap Map material as a source of open-source intelligence, including the limitations of relying on platform-supplied location information.
Isolating lens effects from source camera identification using sensor pattern noise
Research separating contributions from camera optics and sensor behaviour when evaluating image-source characteristics.
Reviewing a police digital-forensic report
A practical starting point for solicitors assessing the link between a technical finding and the inference advanced in a matter.
AIPIO 2026: forensic science in support of intelligence
AIPIO Intelligence 2026 sharpened a question for RHEM Labs: can forensic information about image origin reach analysts early enough to inform a decision?
When is independent digital-evidence review warranted?
Indicators that a focused forensic review may resolve a material uncertainty before a larger examination is commissioned.
ADSTAR 2026: from forensic research to useful capability
ADSTAR changed how RHEM Labs understands the path from a working forensic method to useful capability: mission need, problem ownership, experimentation and operational learning.
What to clarify before instructing a digital-forensic expert
Define the disputed inference, required opinion, available material and deadline before the examination begins.
A timestamp is not a person: digital traces and attribution
Why recording an event, identifying its cause and attributing it to a person are separate forensic questions.
Why a collection of digital traces is not yet a timeline
Ordering records by time is a presentation step; reconstructing events requires attention to meaning, clocks, coverage and uncertainty.
Windows artefacts: looking beneath the tool report
The source record, operating-system behaviour and decoding assumptions matter as much as a report’s label.
What metadata can and cannot establish
Metadata can inform origin, timing and file history, but its source and processing history must be understood.
Digital forensic expert or cybersecurity expert?
Choose expertise by the evidentiary question, the underlying systems and the reasoning required, rather than the job title.
Tool output is not expert interpretation
A software report can organise records; an expert must explain the reasoning that connects those records to the question.
Logical acquisition versus full acquisition: why completeness matters
Acquisition labels describe methods, not a guarantee that every relevant record has been preserved or decoded.
When should the underlying artefacts be examined?
Go beneath a derivative report when the source, processing or interpretation could materially change the answer.
What a digital forensic expert witness does
From a defined technical question to a reasoned opinion, with clear limits and an independent role in the proceeding.
SPOT: from research to representative evaluation
The current stage of Sensor Pattern Origin Testing and the role of operational users, technical partners and independent evaluators.
Three RHEM Labs papers in the ANZFSS Melbourne 2025 supplement
The 2026 Australian Journal of Forensic Sciences supplement includes laboratory work on digital traces, synthetic media and 3D-printed firearm investigations.
Bits, bias, and Blackstone: When machines testify is the digital trace admissible in court?
A publication examining digital traces and their admissibility as evidence in court.
Blueprints to firearms – a practitioner’s guide to investigating clandestine 3D-printed firearm labs
A practitioner-focused publication on the forensic investigation of clandestine 3D-printed firearm laboratories.
Challenges in authenticating synthetic media: forensic insights from 6,000 DALL·E-generated images
Experimental research evaluating image-origin characteristics in camera and DALL·E-generated images.
Determining Image Sensor Temperature Using Dark Current
Research on dark current and image-sensor temperature, forming part of the laboratory’s sensor-pattern research foundation.
Generative Adversarial Networks from a Cyber Intelligence perspective
A research report on applications and readiness of generative adversarial networks in cyber intelligence.
Reverse Engineering the Raspberry Pi Camera V2: A study of Pixel Non-Uniformity using a Scanning Electron Microscope
Research into pixel non-uniformity in the Raspberry Pi Camera V2, using scanning electron microscopy.
An analysis of optical contributions to a photo-sensor’s ballistic fingerprints
Research examining optical contributions to image-sensor fingerprints.
Towards a unified theory of sensor pattern noise
Doctoral research on dark current, lens effects and temperature bias in CMOS image sensors.