Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users like forensic analysts need insight into the rationale behind the detection. Despite advancements, current approaches suffer from two critical deficiencies: (1)vulnerability to image quality degradation: detection accuracy plummets on low-quality samples, while naive augmentation strategies may induce feature drift and impair performance as diversity expands. (2) factually flawed explanations: explanation models may omit manipulation evidence or hallucinate irrelevant details, undermining interpretability. To address it, we propose a framework with two innovations. For robust deepfake detection, we introduce Feature-robust Augmentation, which comprises diversified degradation-aware augmentation strategies, and a supervised contrastive learning pattern paired with a mean-teacher architecture that stabilizes features against augmentations through consistency constraints. For explanation, we devise an evidence-grounded preference optimization process that guides model to prioritize genuine manipulation traces by learning from chosen-rejected explanation pairs, where rejected samples are constructed via evidence omission or irrelevant information injection. The proposed approach wins the first place in ACM Multimedia 2026 Explainable Deepfake Detection Challenge.The code is available at https://github.com/oceanflowlab/EDD.git.
Figures & tables
Figure 1. Illustration of analysis for detection robustness. (a) Performance drops under quality degradation. (b) Mean-teacher stabilizes learning under diverse augmentations while the baseline suffers from feature drift.
Figure 2. Overview of our proposed framework. Given an input image, the visual backbone extracts features and fed into a classifier for prediction. During training, the detection branch incorporates degradation-aware augmentations, supervised contrastive learning, and mean-teacher consistency regularization to enhance robustness against quality degradation and feature drift. For explanation generation, the image and deepfake label are sent to the MLLM for generation. To enhance evidence accuracy and completeness, we propose an evidence-grounded optimization process, which generates evidence-omitted and irrelevant information induced explanation to construct rejected samples, guiding the model via the DPO algorithm. We also provide a concise explanation model that is trained via GRPO with tailored semantic fidelity and conciseness rewards.
3 types: per-channel, grayscale, correlated ; two regimes: σ∼U(2,100) and σ∼U(80,100)
Non-Gaussian Noise
Speckle ( σ∼U(2,25) )/ Poisson
JPEG Compression
Quality factor ∼U(10,95)
Color Enhance
Brightness or contrast factor ∼U(0.5,1.5)
Table 1. Summary of degradation strategies and intensities.
Team name
Overall Score
Detection F1
Complex BERT
Simple Overall
Explanation Score
Rank
Pixel Slueth(Ours)
0.841617
0.942399
0.706168
0.775501
0.740834
1
Team Antvengers
0.838154
0.947941
0.696179
0.760555
0.728367
2
MSUteam
0.8312
0.933982
0.70113
0.755707
0.728419
3
HIT VIRLAB
0.822242
0.926326
0.698713
0.737602
0.718157
4
Team1
0.814503
0.916683
0.696807
0.727839
0.712323
5
Qwen3-VL-8B ( Narang et al., 2026b )
0.642776
0.634234
0.664774
0.637863
0.651318
-
Table 2. Public leaderboard Results of Top-5 Teams. Qwen3-VL-8B/InternVL3.5-13B are challenge ( Narang et al., 2026b ) baselines.
Team
Detection F1
Complex Bert
Simple Bert
SLE Score
Entity F1
Claim F1
Explain Score
Overall Score
Rank
Pixel Sleuth(Ours)
0.9424
0.7063
0.6819
0.9782
0.5280
0.4205
0.5800
0.7612
1
Antvengers
0.9479
0.6958
0.6699
0.9720
0.5078
0.3940
0.5618
0.7549
2
MSUteam
0.9340
0.7004
0.6509
0.9726
0.4987
0.3932
0.5571
0.7456
3
Team1
0.9167
0.6959
0.6321
0.9335
0.5155
0.4021
0.5590
0.7378
4
HIT VIRLAB
0.9263
0.6988
0.6493
0.9287
0.4467
0.3436
0.5235
0.7249
5
Table 3. Final hidden-test results for Top-5 team.
Figure 3. Visualizations for deepfake explanation. Result of baseline ( Yang et al., 2025 ) shows irrelevant information, fails to identify valid evidence. SFT-only model captures partial evidence (marked in green) but still suffers irrelevant details(marked in red). Our model generates complete and accurate explanation(marked by green) without introducing spurious information.
Figure 4. Qualitative comparison of detection robustness under samples with various image degradations. The baseline model misclassifies degraded samples, while our model shows correct predictions across different degradation types.
Configuration
Detection F1
Detection Accuracy
Baseline
0.8824
0.8877
+ DA
0.9402
0.9411
+ DA& SC
0.9588
0.9623
+ DA& MT
0.9593
0.9627
Full(+ DA& MT & SC)
0.9724
0.9793
Table 4. Ablation on detection components.
Training Strategy
Fact F1
Claim F1
BERTScore
Baseline
0.4402
0.3362
0.6038
+ SFT
0.5173
0.4218
0.6673
+ DPO (Omit Only)
0.5666
0.4860
0.6912
+ DPO (Irrelevant Only)
0.5743
0.5042
0.6856
+ DPO (Irrelevant + Omit )
0.5942
0.5144
0.7131
Full(DPO with all three types)
0.5969
0.5227
0.7184
Table 5. Ablation on complex explanation model designs.
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.
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +4
American University of Sharjah Sharjah, United Arab Emirates
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.
Artem Filippov, Aleksandr Gushchin, Kirill Koltsov +2
Lomonosov Moscow State University Moscow, Russia · MSU Institute for Artificial Intelligence Moscow, Russia
As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user trust. Existing benchmarks mainly evaluate classification accuracy, overlooking whether explanations reflect the actual manipulations. This gap hinders progress toward deployable, explainable deepfake detection systems. To this end, we introduce XPlainVerse, a large-scale benchmark designed for joint deepfake detection and human-centered explanation. XPlainVerse comprises one million real and manipulated images, pairing authentic images from five established sources with forgeries generated by twelve off-the-shelf image editing and synthesis models. We further propose a multi-stage filtering pipeline, Edit-Check, to verify if manipulations satisfy their intended edits, enabling reliable reasoning supervision at scale. Beyond dataset scale, XPlainVerse provides two complementary explanation styles: technical explanations for expert analysis and simplified explanations optimized for non-technical users. To evaluate explanation quality beyond surface similarity, we propose novel metrics, EntityScore and EvidenceScore, that measure reasoning fidelity by checking whether explanations correctly identify manipulated entities and visual evidence. Human annotations on 2,000 explanation pairs validate our dataset quality against human judgment. We believe XPlainVerse will establish grounded explanation quality as a measurable dimension of deepfake detection and support scalable research on trustworthy, interpretable models.
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +3
Monash University · MBZUAI · The University of Queensland