---
title: 'Skyra: Explainable Video Forgery Detection'
url: https://www.emergentmind.com/topics/skyra
type: topic
---

# Skyra: Explainable Video Forgery Detection

Skyra is a specialized multimodal large language model (MLLM) designed to detect AI-generated videos by identifying and localizing human-perceivable visual artifacts, providing not only precise detection but also human-interpretable, grounded explanatory evidence. Unlike binary classifiers that output only a “Real” or “Fake” verdict, Skyra emulates human reasoning by analyzing spatio-temporal inconsistencies, generating a chain-of-thought (CoT) that details visual artifacts linked to synthetic content. To support this capability, Skyra leverages ViF-CoT-4K, a large-scale, fine-grained, human-annotated dataset of video artifacts, and achieves robust performance through a two-stage supervised and reinforcement learning strategy. Evaluations demonstrate state-of-the-art accuracy and explanation fidelity across diverse, modern AI video generators and robustness scenarios [2512.15693].

## 1. Conceptual Foundations and Objectives

Skyra addresses escalating concerns regarding the misuse of AI-generated video by advancing both explainability and detection reliability. Its core objectives are:

- Accurate classification of videos as “Real” or “Fake,” surpassing standard binary detectors.
- Generation of explicit, human-interpretable explanations, including annotation of detected artifacts with category, spatio-temporal localization (bounding boxes and time spans), and natural language justification.
- Facilitation of human-in-the-loop auditing by providing chain-of-thought reasoning and artifact evidence, enhancing model transparency.

Skyra operationalizes these objectives by modeling the human strategy of interrogating videos for spatio-temporal inconsistencies, outputting a structured protocol for both the detection process and its justifications.

## 2. Model Architecture and Output Structure

Skyra extends the Qwen2.5-VL-7B-Instruct MLLM as its base model with the following architectural components:

- **Visual Encoder**: Uniformly samples $T=16$ frames at $256p$ resolution per video, feeding them into a pretrained vision transformer to produce per-frame feature maps.
- **Text Encoder**: Tokenizes all input prompts, including interleaved “[T= ] <image>” markers for temporal correspondence, using a transformer architecture.
- **Fusion and Decoder**: Cross-modal cross-attention layers fuse visual and textual embeddings. An auto-regressive language decoder then outputs:
    - A `<thinking>` block: chain-of-thought reasoning detailing artifact identification and justification.
    - A `<answer>` token: either “Fake” or “Real.”
    - For “Fake”: Auxiliary tags including `<type>…</type>`, `<t>[t_start,t_end]</t>`, and `<bbox>[x_min,y_min,x_max,y_max]</bbox>` for artifact type and localization.
    - For “Real”: The same tag format, used as structured “inspection evidence” indicating systematic artifact search yielded no findings.

This output format enables both automated and human review of detections and rationales.

## 3. ViF-CoT-4K: Dataset and Annotation Protocol

The ViF-CoT-4K dataset is central to Skyra’s methodology, comprising:

- **Data Composition**: Over 4,000 AI-generated videos (from generators such as Wan 2.1, Wan 2.2, CogVideoX, HunyuanVideo) paired with a roughly equal number of real videos derived from Panda-70M, Kinetics-400, and HD-VILA-100M.
- **Artifact Taxonomy**:
    - Level 1: Low-Level Forgery (17.2%), Violation of Laws (82.8%)
    - Level 2: Eight subcategories (e.g., Texture Anomaly, Object Inconsistency, Violation of Commonsense)
    - Level 3: Eighteen fine-grained artifact types (e.g., “Structure Anomaly,” “Unnatural Human Movement,” “Text Distortion”)
- **Annotation Schema**: Each fake video includes (1) artifact type (L3), (2) textual explanation, (3) temporal span $[t_{\mathrm{start}}, t_{\mathrm{end}}]$, (4) spatial bounding box $(x_{\min}, y_{\min}, x_{\max}, y_{\max})$, and (5) a paired annotation on the corresponding real video.
- **Chain-of-Thought Expansion**: Concise human labels are expanded into multi-step reasoning scripts using Gemini-2.5-Pro prompts, adhering to a standardized observe–understand–draft–review–conclude protocol in JSON format.

A data analysis of ViF-CoT-4K shows dominant artifact types: Object Inconsistency (28.1%), Violation of Commonsense (27.8%), Texture Anomaly (11.2%), among others.

## 4. Training Strategy: Supervised Fine-Tuning and Reinforcement Learning

Skyra training proceeds in two distinct stages:

### 4.1. Supervised Fine-Tuning (Skyra-SFT)

- **Initialization**: From Qwen2.5-VL-7B-Instruct, using ViF-CoT-4K for targets.
- **Inputs**: Frames $v$ plus prompt $t$ (system and user chain-of-thought template).
- **Targets**: Full token sequence $y^*$ including CoT reasoning, `<answer>`, and artifact localization tags.
- **Loss Function**: Cross-entropy loss over all output tokens,
    $$
    \mathcal{L}_{\mathrm{SFT}} = -\sum_{t=1}^T \log p_\theta(y^*_t \mid y^*_{<t}, t, v)
    $$
- **Optimization**:
    - Learning rate $= 1 \times 10^{-5}$
    - Batch size 1 per GPU, 5 epochs, 8×H200 GPUs.

### 4.2. Reinforcement Learning (Skyra-RL)

- **Algorithm**: Group Relative Policy Optimization (GRPO).
- **Reward Function**: Weighted sum of accuracy ($w_{\mathrm{acc}}=0.8$) and inspection ($w_{\mathrm{chk}}=0.2$) rewards:
    $$
    R(x, y) = w_{\mathrm{acc}}\,r_{\mathrm{acc}}(x,y) + w_{\mathrm{chk}}\,r_{\mathrm{chk}}(x,y)
    $$
    - Asymmetric accuracy reward:
    $$
    r_{\mathrm{acc}}(x,y) = \begin{cases}
        +1.0 & y_{\mathrm{pred}} = y_{\mathrm{gt}} \\
        0.0  & y_{\mathrm{gt}} = \mathrm{Fake},\,y_{\mathrm{pred}} = \mathrm{Real} \\
        -0.2 & y_{\mathrm{gt}} = \mathrm{Real},\,y_{\mathrm{pred}} = \mathrm{Fake}
    \end{cases}
    $$
    - Inspection reward incentivizes correct structured outputs and at least one valid artifact check block.
- **KL Regularization**: Coefficient 0.02, actor learning rate $=5 \times 10^{-7}$.
- **Duration**: Approximately one epoch (4,000 samples) for RL refinement.

Ablation studies reveal that both CoT supervision and the two-stage training are critical for high performance, with major accuracy and F1 drops if either is omitted.

## 5. Evaluation: Benchmarks, Metrics, and Results

Skyra's efficacy is validated through extensive benchmarking:

- **Benchmarks**:
    - ViF-Bench: 3,000 videos from 11 state-of-the-art video generators, matched in semantics and structure for real/fake.
    - GenVideo: 8,000 out-of-domain (OOD), lower-quality videos from older models.
    - Robustness: JPEG compression, geometric transforms, Gaussian noise, lighting/color jitter.
- **Metrics**: Accuracy (Acc), Recall on “Fake” class (R), F1-score.

### Key Quantitative Results (ViF-Bench)

| Method          | Acc   | R      | F1    |
|-----------------|-------|--------|-------|
| DeMamba         | 64.29 | 96.66  | 73.00 |
| Qwen2.5-VL(7B)  | 50.57 | 3.53   | 6.63  |
| BusterX++       | 56.90 | 14.40  | 21.94 |
| **Skyra-SFT**   | 90.11 | 84.65  | 88.76 |
| **Skyra-RL**    | 91.02 | 88.35  | 90.27 |

- Skyra-SFT outperforms the best binary detector by +25.8% accuracy and +18.7% F1-score.
- Skyra-RL further improves accuracy (+0.9%) and F1 (+1.5%), primarily by enhancing recall on difficult I2V samples.
- On OOD GenVideo (without extra labels), Skyra-RL achieves 57.21% accuracy (+11.07% over DeMamba). After one epoch of RL using 2,200 GenVideo samples, Skyra-RL-GenVideo yields 91.00% accuracy, 87.66% recall, and 90.00% F1.

### Robustness and Qualitative Examples

Skyra-RL maintains >80% accuracy and F1 under all tested data corruptions. Qualitative outputs include precise artifact descriptions (e.g., “Structure Anomaly at T=[1.5–2.0]s, bbox=[…]: twisted fence lines flickering”) and structured negative findings for real videos.

## 6. Strengths, Limitations, and Future Directions

**Strengths**:
- Detection accuracy exceeding 90% on in-distribution data with strong OOD generalization.
- Fine-grained, grounded explanations coupling artifact type with spatio-temporal evidence.
- Modular transferability: the combination of cold-start SFT and RL supports rapid adaptation to new domains using minimal incremental supervision.

**Limitations**:
- Coverage is restricted to 16-frame clips and contemporary T2V/I2V model artifacts.
- Model explanations may hallucinate or cite non-causal features, indicating imperfect fidelity in artifact localization.
- Skyra does not address intent, broader contextual analysis, or assessment of societal harm, focusing instead on evidence-driven auditing workflows.

**Future research aims**:
- Expansion to ultra-long videos (>1 minute) and non-photorealistic or stylized generative models.
- Incorporation of uncertainty estimation and mechanisms for calibrated active human feedback.
- Distributed or multitask architectures integrating detection with watermarking or provenance tracking.
- Exploration of hierarchical spatio-temporal attention mechanisms to further increase the localization resolution of detected artifacts.

Skyra exemplifies a new paradigm in explainable video forgery detection, combining high-precision verdicts with structured, inspectable chains of reasoning [2512.15693].

Source: https://www.emergentmind.com/topics/skyra