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XAI-Deepfakes

FreeInstitutional

Publisher: github.com/IDT-ITI/XAI-Deepfakes | Type: Detector | Cost: Free

TL;DR

A free, open-source academic toolkit from CERTH IDT-ITI that runs Explainable AI methods (GradCAM, SHAP, LIME) on an EfficientNet deepfake classifier to produce visual heatmaps showing which pixels drove the verdict. The primary 1C.3 entry for evidence-grade reporting where a probability score is not enough.

What it does

XAI-Deepfakes wraps three Explainable AI methods around an EfficientNet-based deepfake classifier. GradCAM produces gradient- weighted class-activation heatmaps that visualise which regions of the image most contributed to the deepfake verdict; SHAP produces Shapley-value attributions that quantify each pixel's marginal contribution to the classification; LIME produces locally interpretable model-agnostic explanations that approximate the classifier's behaviour on perturbed inputs. The output is a combination of the underlying classifier's verdict plus the three explanation visualisations, which together form an evidence artefact a fact-checker can publish alongside a detection finding and a defamation-defence file can attach to legal pleadings.

The toolkit's institutional value sits in defamation-defence contexts. Where Sensity and Reality Defender produce probabilistic scores plus vendor-defined "explainable indicators" inside a black-box pipeline, XAI-Deepfakes produces the academic state-of- the-art explainability output with reproducible methods. For a court or platform-appeal process that asks "what made the system decide this was AI?", XAI-Deepfakes can answer the question with visualisations that are scientifically grounded rather than vendor-defined.

When to use it

  • A defamation defence file requires evidence-grade detection output: not just a probability score but a visual map of which pixels drove the verdict, attached to the published debunk as legal-defensibility material.
  • A research-grade fact-check needs to show that a Sensity or Reality Defender verdict is corroborated by an academic classifier with explainability output, not only by a black-box vendor pipeline.
  • A regional academic partner is producing a forensic report and the methodology must be reproducible by another lab from the published code.
  • A graduate-school or partner-university context teaches detector explainability and the GradCAM / SHAP / LIME triad is the methodologically appropriate teaching environment.

Independent accuracy

Vendor claim vs independent assessment

Vendor positioning: XAI-Deepfakes is positioned as a methodology toolkit for explainable deepfake forensics rather than as a headline-accuracy classifier. The underlying EfficientNet classifier is a standard academic baseline; the three Explainable AI methods (GradCAM, SHAP, LIME) are canonical in the explainability literature. The maintainer does not market a single headline accuracy figure.

No independent SEA-specific benchmark identified as of May 2026. XAI-Deepfakes is academic tooling and has not been independently audited for SEA-specific performance. The underlying EfficientNet classifier inherits the Deepfake-Eval-2024 benchmark cross- category gap: no Asian-face or SEA-language breakdown is publicly available for image / video detection methodology of this generation. The toolkit's editorial framing is that XAI-Deepfakes' value sits in the explainability output rather than in the underlying classifier's headline accuracy; the classifier should be treated as one weak signal class per Anchor 1, with the explainability output supporting the interpretation work that makes the signal usable in an evidence-grade report.

Limitations

Limitations

  • Requires Python proficiency, an Ubuntu environment, and a GPU for practical performance.
  • Academic tooling, not production. Maintenance follows the research lab's grant cycle rather than a vendor SLA, and operationalising it for a working newsroom requires the newsroom's own technical staff.
  • The underlying EfficientNet classifier inherits the same Asian-face / SEA-language benchmark gap that affects every detector in the toolkit (the Deepfake-Eval-2024 cross-category note).
  • GradCAM / SHAP / LIME outputs are interpretable but not objective: an explanation map shows what the classifier attended to, not what is true about the image. Treat explanations as evidentiary aids, not as ground truth.
  • No Lao-language, Sinhala, or Tamil documentation; the toolkit is English-only academic material.

Privacy and threat model

XAI-Deepfakes runs locally on the analyst's GPU machine. The suspect file stays under the analyst's control during analysis; no upload to a vendor cloud is required. This makes the toolkit the right privacy posture for surveillance-environment work in Sri Lanka and Laos (where shipping a suspect file to a vendor cloud would itself constitute a disclosure event) and for defamation-defence work generally, where the suspect file may be sub judice and must remain within the analyst's controlled environment.

The threat model that matters here is downstream: an explanation artefact published alongside a debunk is published research output that adversaries can study. Adversarial deepfake research routinely optimises against published explainability methods to produce content that GradCAM / SHAP / LIME visualise as "real" even when the underlying classifier scores it as fake. Treat explanation outputs as evidence in the published analysis rather than as a permanent solution to detection.

Country and platform applicability

  • Indonesia: language-agnostic for visual modules; available in principle to Indonesian academic and newsroom- technical partners with GPU infrastructure.
  • Laos: language-agnostic; the local-execution model is privacy-defensible. No documented Lao-specific deployment.
  • Malaysia: language-agnostic; available to MIMOS, Mesolitica, or university partners with GPU access.
  • Philippines: language-agnostic; available to UP / DLSU / Ateneo and to Rappler's technical layer; particularly relevant for defamation-defence work tied to Rappler / #FactsFirstPH published debunks.
  • Sri Lanka: language-agnostic for visual modules; LIRNEasia and Watchdog technical capacity can run XAI-Deepfakes inside their research environment; explainability output is particularly valuable under Sri Lanka's OSA framework where evidentiary defence matters.
  • Thailand: language-agnostic; available to Thai university partners; relevant under the Thai lèse-majesté and CCA evidentiary regime where explainable detection output reduces legal exposure.

Platform applicability: not platform-specific. Operates on any image or video file the analyst can supply.

How to access

Free download from the IDT-ITI XAI-Deepfakes GitHub repository. Clone the repo, install Python dependencies, configure CUDA for GPU support per the README. No account creation, no beta gate. The toolkit is open-source academic code with a permissive research licence.

Cost (current as of 2026-05)

Free in software terms. The structural cost is hardware: a GPU (Ubuntu host), at least 16 GB GPU memory recommended for EfficientNet inference plus SHAP attribution at usable speeds. For institutional partners with university or research-lab GPU access, this is a non-issue; for a frontline newsroom without GPU infrastructure, the path is partner-mediated rather than direct.

Quickstart

  1. Confirm an Ubuntu (or Linux compatible) host with a CUDA- capable GPU and at least 16 GB GPU memory.
  2. Clone the IDT-ITI XAI-Deepfakes repository from GitHub.
  3. Install Python dependencies and configure CUDA per the README.
  4. Place the suspect image or video frames in the input directory.
  5. Run the GradCAM analysis to produce a class-activation heatmap.
  6. Run the SHAP analysis to produce per-pixel attribution scores (this is the slowest of the three but the most quantitatively defensible).
  7. Run the LIME analysis to produce a locally interpretable approximation of the classifier's behaviour on the input.
  8. Combine the three explanation visualisations alongside the underlying EfficientNet score in a single forensic-report artefact for publication or legal use.

In the toolkit's workflow

Cautious-detector pillar tool with explanation output per Architectural Anchor 1. Sits in 1C.3 Institutional explainable AI forensics as primary, ahead of TruFor (alternative, non-detector reliability map) and TRIED Benchmark (institutional reference framework, not a deployable tool).

Standard combinations:

  • With Sensity at 1C.2 for institutional-pipeline corroboration: Sensity provides the production-grade verdict, XAI-Deepfakes provides the academic explanation artefact that defamation defence can attach.
  • With TruFor at 1C.3 for parallel non-detector reliability-map output on the same suspect image; the two together give per-pixel explanation (XAI-Deepfakes) plus reliability-of-the-reliability-map (TruFor).
  • With DeepfakeBench at 1C.2 inside a research- evaluation context where XAI-Deepfakes' EfficientNet baseline is being compared against the harness's 36 detectors.
  • With TRIED Benchmark at 1C.3 as the sociotechnical evaluation framework that complements XAI-Deepfakes' technical explanation output.

This tool's verdict is one weak signal class per Anchor 1; never publish a binary claim from it alone. The explanation visualisation adds evidentiary weight to a published claim but does not by itself constitute a non-detector signal under Anchor 2's two-non-detector-signal floor.

Decision-tree references: T6 source-protection routes through XAI-Deepfakes' local-execution model when the suspect file cannot leave the analyst's machine. T5 escalation references the explanation artefact at T5.15 (method note) when the published debunk needs a defamation-defence layer.

Conflict resolution behaviour

When XAI-Deepfakes' verdict disagrees with another detector (Sensity, Reality Defender, Hive), XAI-Deepfakes carries weaker headline-accuracy weight than the enterprise platforms because its underlying EfficientNet classifier is an academic baseline rather than a Sensity-grade ensemble. It carries stronger evidentiary weight than the enterprise platforms in defamation-defence contexts because the explanation output is reproducible and methodologically grounded, where vendor "explainable indicators" are vendor-defined and not openly reproducible. When XAI-Deepfakes disagrees with a non-detector signal (provenance manifest, reverse-image hit, archived earlier instance), the non-detector signal wins because Anchor 2 mandates two non-detector signals before any strong public claim. The explanation artefact is most useful when it visualises why the underlying detector and a non-detector signal agree, not when it tries to substitute for the non-detector signal.

Override notes

Override notes

  • Cautious-detector with explanation (g1-cautious-detector- with-explanation): XAI-Deepfakes serves the cautious- detector pillar with an explanation-output supplement. The detector verdict is one weak signal class per Anchor 1; the explanation output supports interpretation rather than adding an independent signal.

  • Defamation-defence pointer (defamation-defence-use-case- pointer): the toolkit's published-output use case targets defamation defence and platform-appeal evidentiary work. Country and platform applicability section above names the Sri Lanka OSA, Thai lèse-majesté and CCA, and Philippine COMELEC contexts where explainability output materially reduces legal exposure on a published debunk (regional legal-context research).

Sources

  • IDT-ITI, Centre for Research and Technology Hellas. XAI-Deepfakes — explainable deepfake detection. GitHub repository (open-source, CUDA/Ubuntu). github.com/IDT-ITI/XAI-Deepfakes.