OmniFake: Unified Fake-Content Detection

• OmniFake is a unified fake-content detection framework that consolidates diverse benchmarks to support joint detection, localization, and explanation.
• It bridges multiple modalities—image, text, audio, and video—with robust performance under out-of-distribution and domain shift conditions.
• Empirical evaluations across variants show high accuracy and generalization, validating its approach for deepfake, misinformation, and synthetic image analyses.

OmniFake denotes not one fixed dataset but a cluster of recent research efforts aimed at unified fake-content detection under heterogeneous generation, manipulation, and deployment conditions. The name appears both as “OmniFake” and “Omni-Fake,” and is attached to image-only, image–text, and fully multimodal benchmarks as well as to capability descriptions centered on joint detection, localization, and explanation. Common across these usages are broad modality or domain coverage, explicit stress on out-of-distribution robustness, and a move away from siloed expert detectors toward single systems that return structured, interpretable outputs (Wen et al., 19 Mar 2025, Li et al., 2 May 2026, Wu et al., 30 Sep 2025, Li et al., 30 Sep 2025, Li et al., 23 Mar 2026).

1. Terminological scope and recurring design pattern

Recent usage is polysemous. In one line of work, OmniFake names a unified multimodal social-media deepfake benchmark with a paired detector. In another, it names a class-aware synthetic-image dataset for open-set detection and few-shot attribution. A third usage denotes an omnibus image–text misinformation benchmark that mixes human-crafted and AI-synthesized deception. A fourth defines a hierarchical benchmark for deepfake generalizability across progressively harder distribution shifts. Related image-forensics papers use “OmniFake-style” or “omni-domain” language to describe the capability target even when the benchmark itself has a different name (Li et al., 2 May 2026, Wu et al., 30 Sep 2025, Li et al., 30 Sep 2025, Li et al., 23 Mar 2026, Wen et al., 19 Mar 2025, Team, 13 May 2026).

Usage	Scope	Reported scale
Omni-Fake (Li et al., 2 May 2026)	Image, audio, video, audio–video talking head	1,137,000 in Omni-Fake-Set; 209,000 in Omni-Fake-OOD
OmniFake (Wu et al., 30 Sep 2025)	Class-aware synthetic image detection and attribution	2.34M train; 180K test
OmniFake (Li et al., 30 Sep 2025)	Image–text multimodal news deception	127,283 samples
OmniFake (Li et al., 23 Mar 2026)	Hierarchical deepfake generalization benchmark	Over 200K images

Across these variants, the recurring technical motif is unification along at least one of three axes: modality unification, domain unification, or task unification. The task axis is especially stable. Detection is rarely treated as an isolated binary score; the more recent formulations couple it with localization, attribution, or natural-language explanation. This suggests that “OmniFake” functions less as a single benchmark name than as a research program for unified, OOD-aware, explanation-capable fake-content analysis.

2. Image-centric OmniFake-style authenticity assessment

In the image modality, an OmniFake-style capability is explicitly defined as a single system that can reliably detect synthetic content across diverse AIGC sources and explain the artifacts that drive its decisions. “Spot the Fake” instantiates this with FakeVLM, a large multimodal model for both general synthetic image detection and DeepFake detection, and FakeClue, a dataset with over 100,000 images across seven categories—Animal, Human (people), Object, Scenery (landscape), Satellite (remote sensing), Document, and DeepFake (faces)—annotated with fine-grained artifact clues in natural language (Wen et al., 19 Mar 2025).

FakeVLM follows LLaVA-v1.5. Its visual front end is CLIP ViT-L/14 at $336 \times 336$ resolution, producing

$$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$

A two-layer MLP adaptor maps visual features into the language-model space,

$$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$

with $W_1 \in \mathbb{R}^{1024 \times 4096}$ and $W_2 \in \mathbb{R}^{4096 \times 4096}$, and Vicuna-v1.5-7B serves as the language backbone. The training objective is standard next-token log-likelihood over aggregated annotation text conditioned on vision features and the prompt $P$:

$$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$

This design deliberately avoids a separate classification head and instead trains the model to emit authenticity judgments together with artifact explanations.

The dataset side is equally central. FakeClue aggregates candidate captions from Qwen2-VL, InternVL, and Deepseek into a unified annotation $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$. The aggregation extracts common points, filters out weak or irrelevant single-model observations, and organizes explanations hierarchically by artifact categories such as texture, geometry, and lighting. The annotations emphasize direct synthesis artifacts—structural distortions, texture inconsistencies, lighting anomalies—rather than tampering artifacts concentrated at boundaries. For high-quality synthetic images with no obvious artifacts, special tags instruct the model not to force artifact explanations.

Empirically, FakeVLM reports Acc $0.986$, F1 $0.981$, ROUGE_L $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$0, and CSS $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$1 on FakeClue; on LOKI it reports Acc $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$2, F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$3, ROUGE_L $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$4, and CSS $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$5. On DD-VQA it reports Acc $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$6, F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$7, ROUGE_L $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$8, and CSS $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$9. On FF++ its average AUC is $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$0, and on DMimage it reports Overall Acc $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$1 and Overall F1 $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$2. The ablation between a linear head and explanatory-text training is especially consequential: on LOKI, “LLaVA + Linear Head” reaches Acc $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$3, F1 $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$4, while “LLaVA + Explanatory Text” reaches Acc $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$5, F1 $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$6, ROUGE_L $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$7, and CSS $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$8. Within this image-only branch, OmniFake is therefore best understood as a unified detect-and-explain objective rather than merely a label space.

3. Omni-Fake as a unified multimodal social-media benchmark

“Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection” defines Omni-Fake as a large-scale, social-media-grounded benchmark spanning four modalities—image, audio, video, and audio–video talking head—and supporting a joint detection–localization–explanation protocol. The benchmark comprises Omni-Fake-Set, with 1,137,000 samples, and Omni-Fake-OOD, with 209,000 fully disjoint out-of-distribution samples intentionally excluded from training. Image, audio, and generic video use three-way labels $$H = \mathrm{GeLU}(VW_1 + b_1), \qquad Z = HW_2 + b_2,$$9, while AV-TH uses binary labels $W_1 \in \mathbb{R}^{1024 \times 4096}$0 (Li et al., 2 May 2026).

The scale is explicitly modality-specific. Omni-Fake-Set contains 792k images, 120k audio samples, 210k videos, and 15k AV-TH samples. Omni-Fake-OOD contains 98k images, 100k audio samples, 3k videos, and 8k AV-TH samples. The data sources are generator-diverse and split-disjoint: for example, image OOD includes GPT-4o, Ideogram 3.0, and Nano Banana; video OOD includes Sora, Pika, and Runway Gen3; audio OOD includes Higgs-Audio, CosyVoice, and Fish Speech; AV-TH OOD includes deepspeak-v2, Ditto, and ACTalker. Localization annotations are spatial for images and videos, temporal for audio and videos, and explanations are evaluated with ROUGE-L, Cosine Semantic Similarity, and human expert ratings on factual correctness and usefulness.

Omni-Fake-R1 is built on Qwen2.5-Omni-7B and trained in two stages. The supervised phase uses a four-stage curriculum with modal replay: audio $W_1 \in \mathbb{R}^{1024 \times 4096}$1 images $W_1 \in \mathbb{R}^{1024 \times 4096}$2 videos $W_1 \in \mathbb{R}^{1024 \times 4096}$3 AV-TH, with 15% replay from earlier modalities at each stage. Reinforcement learning then applies unified GSPO under a composite scalar reward

$W_1 \in \mathbb{R}^{1024 \times 4096}$4

with $W_1 \in \mathbb{R}^{1024 \times 4096}$5, $W_1 \in \mathbb{R}^{1024 \times 4096}$6, $W_1 \in \mathbb{R}^{1024 \times 4096}$7, and $W_1 \in \mathbb{R}^{1024 \times 4096}$8. The reward checks structured output tags, class-sensitive detection correctness, spatial IoU on tampered images and videos, and interval IoU on tampered audio and video.

Validation results on Omni-Fake-Set are reported as follows: images Acc $W_1 \in \mathbb{R}^{1024 \times 4096}$9, F1 $W_2 \in \mathbb{R}^{4096 \times 4096}$0, IoU $W_2 \in \mathbb{R}^{4096 \times 4096}$1, Loc F1 $W_2 \in \mathbb{R}^{4096 \times 4096}$2; videos Acc $W_2 \in \mathbb{R}^{4096 \times 4096}$3, F1 $W_2 \in \mathbb{R}^{4096 \times 4096}$4, IoU $W_2 \in \mathbb{R}^{4096 \times 4096}$5, Loc F1 $W_2 \in \mathbb{R}^{4096 \times 4096}$6; audio Acc $W_2 \in \mathbb{R}^{4096 \times 4096}$7, F1 $W_2 \in \mathbb{R}^{4096 \times 4096}$8, interval IoU $W_2 \in \mathbb{R}^{4096 \times 4096}$9, interval F1 $P$0; AV-TH Acc $P$1, F1 $P$2. On Omni-Fake-OOD, the model reports image Acc $P$3, video Acc $P$4, audio Acc $P$5, and AV-TH Acc $P$6, with corresponding localization gains over strong modality-specific baselines. The explanation study reports that removing explanation-related reward terms reduces CSS while leaving detection nearly unchanged, indicating that RL primarily shapes rationale quality rather than raw classification.

4. OmniFake for open-set synthetic image detection and few-shot attribution

A distinct usage of OmniFake appears in “OmniDFA: A Unified Framework for Open Set Synthesis Image Detection and Few-Shot Attribution,” where OmniFake is a class-aware synthetic-image dataset designed to support both open-set detection and few-shot source attribution. Its organizing principle is architectural distinctness: each generator class is intended to represent a genuinely different architecture rather than a minor variation of the same backbone. The dataset contains 1.17M synthetic and 1.17M real images in the training set, for 2.34M total, and a balanced 180K-image test set with 90K synthetic and 90K real images. The synthetic side spans 45 distinct generative models, including diffusion and flow-matching models, GANs, autoregressive and unified multimodal generators, and closed-source or community models such as Midjourney, DALLE3, Ideogram, and GPT4-o (Wu et al., 30 Sep 2025).

Evaluation is generator-disjoint by construction. The dataset is split into three balanced parts; in each round, two parts are used for training and one for testing, with 15 held-out generators in the test fold. The few-shot attribution protocol uses 5-way 5-shot and 15-way 5-shot episodic testing over unseen classes, with 10,000 independent episodes per configuration. Detection is also evaluated on external datasets such as GenImage and Chameleon, with overlapping families removed from training to avoid leakage.

OmniDFA uses multi-level feature extraction. One pathway preserves aspect ratio and center-crops for global cues; the other takes a high-resolution crop from the original image for local textures. Features are concatenated and projected to $P$7-dimensional embeddings $P$8. Training combines supervised contrastive learning with a real-class center constraint:

$P$9

A single normalized real-class center $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$0 is learned, and the authenticity boundary $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$1 is updated with Tukey’s fences and momentum,

$$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$2

The reported training configuration uses ConvNeXt-Small, AdamW with learning rate $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$3, 20 epochs, cosine annealing, $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$4, $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$5, $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$6, mixed-precision bfloat, and 8$$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$7A100 40GB.

Results separate the two target tasks. For unseen-generator authenticity detection, OmniDFA reports average Acc $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$8 and AP $$\mathcal{L}(\theta) = -\sum_{t=1}^{T_i} \log p_\theta(a_{i,t} \mid a_{i,<t}, [Z; E(P)]).$$9 across the three held-out parts, with per-part Acc $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$0, $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$1, and $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$2. For open-set few-shot attribution, it reports average 5-way 5-shot accuracy $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$3 and 15-way 5-shot accuracy $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$4, outperforming FSD, ComFor, UniversalAttr, SiameseNet, CPL, and DNA-Det in the reported comparisons. Cross-dataset generalization is likewise strong: on GenImage the average accuracy is $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$5, and on Chameleon the overall accuracy is $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$6. In this usage, OmniFake is less a benchmark for explanation than a resource for generator-general detection and source-aware forensic identification.

5. OmniFake as an omnibus multimodal news-deception dataset

A third major usage appears in “Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline,” where OmniFake is the Omnibus Dataset for Multimodal News Deception. This benchmark contains 127,283 English image–text pairs with ternary labels: Real, Human-crafted, and AI-synthesized. The distribution is 49,034 Real posts, 24,726 Human-crafted misleading posts, and 53,523 AI-synthesized multimodal deception samples, with an $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$7 train/validation/test split (Li et al., 30 Sep 2025).

The dataset combines social-media realism with controlled synthetic augmentation. Real and Human-crafted samples are selected from Reddit’s r/Fakeddit corpus. The AI-generated subset is created through several pipelines. For person-containing images, face swap uses SimSwap and E4S, while face attribute editing uses HFGI with DSFD-based face detection, landmark alignment, standardized cropping, and edit-strength factor $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$8. For non-person images, Qwen-2.5-VL generates descriptive prompts, CLIP similarity filtering retains only prompt–image pairs with cosine similarity at least $A_i = \mathcal{M}(A_i^1, A_i^2, A_i^3)$9, SAM2 returns manipulated masks $0.986$0, and SDXL or SDXL Inpainting synthesizes edited images from $0.986$1. Text fabrication uses SpaCy NER and Qwen3 either to rewrite headlines misleadingly while preserving named entities or to replace 2–3 adjectives or short phrases with controlled substitutions, logging replacement positions $0.986$2. For manipulated samples, OmniFake stores modality-specific annotations such as manipulated region masks, prompt pairs $0.986$3, and text replacement logs.

UMFDet is the baseline model. It uses Florence-2 (0.7B) with DaViT as visual encoder and a Category-aware Mixture-of-Experts adapter aligned to the three label categories: Reality, Deception, and Synthesis. Each expert is a two-layer feed-forward block with gated activations,

$0.986$4

followed by

$0.986$5

Routing is hard-gated in Switch style:

$0.986$6

The second mechanism, Attribution chain-of-thought, uses Qwen3 to extract metadata and generate schema-constrained rationales grounded in both image and text evidence. Training optimizes

$0.986$7

On OmniFake, UMFDet reports ACC $0.986$8, Recall $0.986$9, and F1 $0.981$0, with per-category F1 of $0.981$1 for Real, $0.981$2 for Human-crafted, and $0.981$3 for AI-synthesized. On DGM4 it reports ACC $0.981$4, Recall $0.981$5, and F1 $0.981$6. Zero-shot transfer averages are ACC $0.981$7, Precision $0.981$8, Recall $0.981$9, and F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$00 on DGM4 domain splits, and ACC $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$01, Precision $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$02, Recall $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$03, and F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$04 on FineFake. The ablation shows that adding the Category-aware MoE improves performance from ACC $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$05, F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$06 to ACC $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$07, F1 $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$08, while Att-COT improves F1 from $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$09 to $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$10. Here, OmniFake explicitly targets the case in which the deception type is unknown at inference time.

6. Hierarchical generalization and adjacent omni-domain formulations

“VIGIL: Part-Grounded Structured Reasoning for Generalizable Deepfake Detection” introduces yet another OmniFake: a hierarchical 5-Level benchmark designed to evaluate deepfake detectors under progressively harder domain shifts while constraining training to only three foundational generators—StyleGAN2, Stable Diffusion 1.4, and SimSwap—plus real images from DF40. The full benchmark contains over 200K images, filtered and balanced to approximately 105K real and 105K fake, with 40K images in the training split. Level 1 is In-Distribution (13K); Level 2 is Cross-Architecture (24K); Level 3 is Cross-Model (75K); Level 4 is Cross-Task (15K); and Level 5 is In-the-Wild (42K), with Level 5 explicitly including social-media and wild datasets such as DFDC, Chameleon, WildRF, and So-Fake (Li et al., 23 Mar 2026).

The benchmark is organized by shift type rather than merely by generator identity. Level 2 tests within-paradigm cross-architecture transfer with ADM, BigGAN, SDXL, and StyleGANXL. Level 3 introduces entirely unseen families and systems, including FLUX, SD3, NOVA, Harmon, Midjourney, Nano Banana, GPT-Image, Sora2, and Veo 3. Level 4 shifts from full synthesis to localized edits and restorations with BrushNet, CodeFormer, RestoreFormer++, and GFPGAN. Level 5 adds unknown provenance together with real-world degradations such as compression and resizing. This structure turns OmniFake into a graded stress test of hierarchical generalization rather than a single in-distribution benchmark.

VIGIL itself uses a plan-then-examine pipeline with part-centric reasoning and stage-gated injection of part-level forensic evidence. On OmniFake it reports overall Accuracy $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$11, compared with $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$12 for DDA and $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$13 for Veritas. Reported highlights include Level 1 Accuracy $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$14; Level 4 average $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$15; and Level 5 average $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$16, with Level 5 subset results of Chameleon $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$17, DFDC $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$18, So-Fake $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$19, and WildRF $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$20. Under JPEG compression, VIGIL reports $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$21 at QF$$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$22, $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$23 at QF$$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$24, and $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$25 at QF$$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$26; under Gaussian blur it reports $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$27 at $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$28, $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$29 at $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$30, and $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$31 at $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$32.

A related but terminologically distinct line interprets OmniFake as the omni-domain objective for unified Fake Image Detection and Localization rather than as a named benchmark. “Venus-DeFakerOne: Unified Fake Image Detection & Localization” explicitly states that it does not mention any dataset, benchmark, or prior work named “OmniFake,” but addresses the same unified goal across document editing, natural image manipulation, DeepFake generation, and full-image AIGC. DeFakerOne integrates InternVL2-2B and SAM2, is trained on a 12.5M multi-domain corpus, outputs image-level detection and pixel-level masks, and reports state-of-the-art performance on 39 forgery detection benchmarks and 9 localization benchmarks. Domain-level averages are DeepFake $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$33 AUC, AIGC $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$34 ACC, Document $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$35 ACC, and Nature $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$36 AUC; on OpenMMSec it reports $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$37 across DeepFake, AIGC, IMDL, and Doc, with average $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$38; and on GPT-Image-2-Bench it reports $$V = \mathrm{CLIP\text{-}ViT}(I) \in \mathbb{R}^{N \times d_v}, \quad N = HW/P^2, \ d_v = 1024, \ P = 14.$$39 ACC (Team, 13 May 2026).

Taken together, these strands indicate that OmniFake has become a shorthand for unified forensic evaluation under real distribution shift. In some papers it is the dataset name, in others the benchmark name, and in still others a capability target. The stable research claim across all of them is narrower and more technical: fake-content analysis increasingly requires systems that generalize across domains or modalities, preserve localization or attribution structure, and provide explanations that remain useful under unseen generators, localized edits, and social-media degradation.