MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

Artem Filippov; Aleksandr Gushchin; Kirill Koltsov; Dmitriy Vatolin; Anastasia Antsiferova

cs.CVOct 7, 2026

MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

Authors: Artem Filippov, Aleksandr Gushchin, Kirill Koltsov, Dmitriy Vatolin, Anastasia Antsiferova

Organizations: Lomonosov Moscow State University Moscow, Russia · MSU Institute for Artificial Intelligence Moscow, Russia

Abstract

Recent advances in generative image models have made many manipulated images highly realistic, raising the need for detectors that are not only accurate but also able to provide visual evidence for their decisions. In this paper, we present our solution to the Explainable Deepfake Detection Challenge [2] on the XPlainVerse dataset [1], where systems are required to predict whether an image is real or fake and generate both complex and simple explanations grounded in visible forensic cues. Our method follows a modular detection-and-explanation design. For the real/fake decision, we build a multi-backbone detector that combines several DINOv3 models with Mesorch manipulation-localization features, bringing together pretrained visual representations, DCT-aware cues, and multi-scale forensic information. To inject explanation evidence into the detector, we use a Grounding-DINO-based pseudo-mask generation pipeline that converts local artifact descriptions from training explanations into weak patch- level supervision for an Artifact Evidence Map. We further introduce a local patch-level contrastive objective that separates artifact and authenticity evidence in the detector feature space without requiring paired images or pixel-level manipulation masks. For language output, we use class-conditional Qwen3-VL models to generate complex explanations for fake and real predictions, followed by a GRPO-optimized text simplification model. The proposed methods were trained and evaluated on the challenge subset of XPlainVerse. On the full test split, our submission achieves 0.9349 detection accuracy, 0.5571 explanation score, and a 0.7456 final challenge score.

Figures & tables

Figure 1. Overview of the proposed system, including offline pseudo-mask generation, multi-backbone forensic detection, and class-conditional explanation generation. A three-part pipeline: explanation-grounded pseudo-mask generation, a multi-backbone detector with image and patch losses, and class-conditional complex and simple explanation generation.

	CF	UF	WF
CF	pull(CF, CF)	pull(sg(CF), UF)	–
UF	pull(UF, sg(CF))	pull(UF, UF)	–
WF	–	–	–

Table 1. Local contrastive rules for fake–fake patch pairs.

	CR	UR	WR
CR	pull(CR, CR)	pull(sg(CR), UR)	pull(sg(CR), WR)
UR	pull(UR, sg(CR))	pull(UR, UR)	pull(sg(UR), WR)
WR	pull(WR, sg(CR))	pull(WR, sg(UR))	–

Table 2. Local contrastive rules for real–real patch pairs.

	CR	UR	WR
CF	push(CF, CR)	push(sg(CF), UR)	push(sg(CF), WR)
UF	push(UF, sg(CR))	push(UF, UR)	pull(sg(UF), WR)
WF	–	pull(sg(WF), UR)	pull(sg(WF), WR)

Table 3. Local contrastive rules for fake–real patch pairs.

Figure 2. Internal-validation examples. From top to bottom: pseudo boxes, ground-truth simple explanations, Artifact Evidence Maps, and generated simple explanations. Eleven examples showing grounded pseudo boxes, reference simple explanations, detector evidence heatmaps, and generated simple explanations.

Variant	$\lambda_{\mathrm{img}}$	$\lambda_{\mathrm{art}}$	$\lambda_{\mathrm{lcl}}$	Val. AUC
Image-level detector only	15	0	0	0.8975
+ pseudo-mask artifact supervision	15	1	0	0.9139
+ LCL	15	1	1	0.9417

Table 4. Detector ablation on the public validation split.

Method	$B_{c}$	$F_{\mathrm{ent}}$	$F_{\mathrm{evid}}$	$B_{s}$	$L_{s}$	$S_{s}$	$M_{\mathrm{exp}}$
Zero-shot Qwen3-VL-253B-A22B-Instruct	0.6023	0.5310	0.3934	0.4495	0.3418	0.4172	0.4812
Organizer baseline 1 1 1 The organizer baseline model can be found on the official challenge website: https://explainable-deepfake-detection.github.io/	0.7109	0.5536	0.4620	0.4578	0.4264	0.4484	0.5365
Ours	0.7253	0.6235	0.5447	0.4991	0.9703	0.6405	0.6236

Table 5. Explanation generation evaluation on the internal held-out validation subset.

B_{c}

and

B_{s}

denote complex and simple BERTScore-F1;

F_{\mathrm{ent}}

and

F_{\mathrm{evid}}

denote Entity and Evidence F1;

L_{s}

S_{s}

, and

M_{\mathrm{exp}}

denote normalized simple SLE, Simple score, and Explanation score.

Metric	Score
Detection accuracy	0.9349
Detection fake F1	0.9418
Detection macro F1	0.9340
Detection real F1	0.9261
Complex BERTScore-F1	0.7004
Simple BERTScore-F1	0.6509

Table 6. Final submission results on the full final test split.

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Explainable Deepfake Detection Challenge

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American University of Sharjah Sharjah, United Arab Emirates

Image Forgery Detection Explainable Deepfake Detection Deepfake Detection Explainable Image Classification Evidence-Grounded Generation Explainability Evaluation

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Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is important for real-world verification settings, where diverse users need to understand not only whether an image is manipulated, but also why it is considered suspicious. The Explainable Deepfake Detection Challenge at ACM Multimedia 2026 is designed to benchmark this joint capability. Built on XPlainVerse, a million-scale benchmark for explainable deepfake detection, the challenge evaluates methods on image classification and grounded natural-language explanation generation. Participants submit a real/fake label together with two explanations for each image: a detailed complex explanation for technical users and a concise simple explanation for general users. The evaluation combines classification metrics with semantic similarity, simplicity, and intent-aware grounding metrics that assess whether explanations identify the relevant manipulated entities and supporting visual evidence. The methodologies developed through the challenge will contribute to the development of next-generation explainable deepfake detectors. Evaluation script, baseline models, and accompanying code are available on https://github.com/Abhijeet8901/XPlainVerse-ACMChallenge.

Abstract

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