Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Chenfan Qu; Yachao Liang; Mingqi Fang; Jiangwei Xie; Zhenming Wang; Baoyue Zhang; Changjiang Jiang; Zheng Chang

cs.CVOct 27, 2026

Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Authors: Chenfan Qu, Yachao Liang, Mingqi Fang, Jiangwei Xie, Zhenming Wang, Baoyue Zhang, Changjiang Jiang, Zheng Chang,

Organizations: GuangJian Team, Ant Group · Technical Committee on Digital Media Forensics and Security, CSIG

Abstract

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

Figures & tables

Figure 1: DefakerOne2 significantly expands defense coverage along both the forgery-form and media-type dimensions.

Figure 2: DefakerOne2 consistently outperforms its predecessor DeFakerOne and existing state-of-the-art detectors across all image and video forensic domains.

Figure 3: The overall framework of our DefakerOne2.

Figure 4: The proposed Entropy-Based Test Adaptation.

Figure 6: Fake video samples from our Realistic Rumor Video Benchmark (RRVBench), which focuses on real-world rumor scenarios such as military actions, political events, public unrest, health emergencies, etc. They are created by the latest video generators.

Method	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MOE	Ours V1	Ours V2
FF-c23	21.2	73.4	53.9	94.2	92.5	66.7	47.7	99.4	99.5
FF-c40	20.8	79.1	49.2	79.7	80.4	59.2	64.9	84.7	88.2
FF-DF	49.8	40.7	62.1	99.1	99.0	78.8	46.0	99.8	99.8
FF-F2F	50.0	43.8	53.8	93.8	92.0	59.3	36.4	99.2	99.3
FF-FS	50.2	42.6	47.0	96.5	97.2	76.5	43.1	99.5	99.6
FF-NT	49.9	49.7	52.8	87.4	81.8	52.5	34.9	98.8	99.1

Table 1: Image-level AUC comparison with forensic vision models on DeepFake detection benchmarks. F.MoE: abbreviation of ForensicMoE, Ours V1: abbreviation of DefakerOne, Ours V2: abbreviation of DefakerOne2

Method	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MOE	Ours V1	Ours V2
ForenSynths	48.1	57.3	57.6	49.8	74.8	68.6	71.5	77.2	79.0
D.Forensics	21.8	55.2	51.4	59.7	71.2	72.9	70.9	99.5	99.8
GenImage	43.8	55.1	53.4	59.0	90.5	87.8	94.3	99.7	99.9
Chameleon	36.7	37.9	41.2	55.2	63.1	51.6	58.9	84.7	86.8
Fakeinversion	83.8	38.4	36.7	42.3	84.6	52.4	81.2	91.8	98.0
BFree-Online	36.6	67.8	60.7	38.3	55.9	71.0	32.9	65.7	91.7

Table 3: Image-level accuracy comparison with forensic vision models on AI-generated image detection benchmarks. F.MoE: abbreviation of ForensicMoE, Ours V1: abbreviation of DefakerOne, Ours V2: abbreviation of DefakerOne2

Method	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MOE	Ours V1	Ours V2
Doc-FCD	73.2	73.1	68.4	52.3	60.1	77.0	38.9	92.8	99.7
Doc-SCD	69.8	58.7	67.7	39.8	26.7	71.8	24.6	99.8	97.6
Doc-Test	64.3	57.8	60.9	46.9	37.1	66.8	29.8	99.6	98.7
TFR	43.0	51.3	51.8	51.0	50.5	52.2	46.9	99.6	81.8
T-IC13	75.3	61.9	58.8	48.0	19.0	48.9	30.5	54.9	78.5
OSTF	60.3	47.0	59.4	57.7	55.9	57.1	57.9	74.9	91.0

Table 5: Image-level accuracy comparison with forensic vision models on tampered document image detection benchmarks. F.MoE: abbreviation of ForensicMoE, Ours V1: abbreviation of DefakerOne, Ours V2: abbreviation of DefakerOne2

Method	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MOE	Ours V1	Ours V2
CASIAv1	46.8	52.5	93.9	63.9	61.9	52.7	7.7	96.3	94.1
COVERAGE	51.5	40.5	72.3	53.6	49.2	52.9	50.6	75.9	80.0
Columbia	62.3	2.20	99.4	88.0	49.2	48.2	81.7	87.7	98.5
NIST16	56.0	38.7	59.4	61.6	73.4	66.7	3.9	85.1	87.3
CocoGlide	51.9	29.6	67.1	67.8	68.2	72.3	19.2	76.0	83.0
Autosplice	42.9	34.3	64.5	77.7	82.0	75.5	37.4	99.9	98.7

Table 7: Image-level AUC comparison with forensic vision models on manipulated natural image detection benchmarks. F.MoE: abbreviation of ForensicMoE, Ours V1: abbreviation of DefakerOne, Ours V2: abbreviation of DefakerOne2

Methods	F. ${}_{\textrm{Random}}$	F. ${}_{\textrm{Object}}$	F.2-klein	Q.-Edit	S.-Edit	N.B. 2	GPT-I. 2	Avg.	Avg.*
UniFD	50.2	50.8	50.7	50.4	50.7	50.3	50.2	50.5	53.0
LOTA	49.9	49.9	49.9	50.0	49.9	50.0	100.0	57.1	53.2
DRCT	51.0	50.9	50.6	52.2	50.1	57.2	64.0	53.7	60.8
FatFormer	49.9	50.0	50.1	50.1	50.3	50.0	57.0	51.0	-
NPR	45.1	46.0	49.8	55.2	48.8	55.5	80.3	54.4	61.0
SAFE	52.2	51.9	50.0	49.0	48.3	49.5	86.7	55.4	-

Table 9: Average / Balanced accuracy (%) comparison on ManipulationBench. F.

{}_{\textrm{Random}}

and F.

{}_{\textrm{Object}}

denote FLUX-Fill using random and object masks, respectively. F.2-Klein denotes FLUX.2-Klein. Q.-Edit and S.-Edit are abbreviations for Qwen-Image-Edit and Step1X-Edit-v1p2, respectively. Avg. denotes average performance of official pre-trained models. Avg.* denotes average performance of models officially fine-tuned on ManipulationBench’s training split. Unreported results are denoted by “-”

dataset	FLIP	MVP	LCDF	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MoE	Ours V1	Ours V2
CASIA-FASD	98.5	93.8	54.7	25.0	56.2	28.5	45.3	79.0	49.0	25.0	64.3	90.3
DLC2021	55.5	55.5	56.9	44.5	55.3	39.4	34.8	54.7	49.6	44.2	59.6	99.9
GenBuster++	50.5	71.6	49.8	50.0	47.0	51.0	48.8	59.4	49.6	57.8	81.4	88.6
GenBuster-ID	49.1	68.9	51.9	50.0	51.6	50.4	58.0	58.8	71.4	69.7	80.7	99.9
GenBuster-OOD	49.5	77.9	53.3	50.0	44.7	51.7	59.6	54.9	67.7	60.2	83.5	99.6
LOKI	52.5	55.5	55.2	33.6	63.1	35.8	51.1	57.2	63.2	33.6	64.9	92.9

Table 10: Video-level accuracy comparison with forensic vision models on video forgery detection benchmarks. F.MoE: abbreviation of ForensicMoE, Ours V1: abbreviation of DefakerOne, Ours V2: abbreviation of DefakerOne2

Detector	Wan2.2 dyn.	Wan2.2 Light.	LTX	Omni Weav.	Happy Horse	Run way	Kling	SeeDance 2.0	Hai luo	All avg.
Image / Frame-level Forensic Detectors
CNNSpot	35.5	43.8	64.0	45.8	39.2	31.9	45.4	40.4	53.9	44.4
NPR	50.6	55.2	58.6	50.9	54.6	52.3	44.3	48.7	54.6	52.2
UnivFD	32.7	43.0	51.2	39.9	43.0	38.4	39.1	35.0	44.0	40.7
ForgeLens	60.0	69.6	64.1	63.0	64.9	59.5	59.2	48.1	61.7	61.1
Video / Temporal-level Forensic Detectors

Table 14: Video-level AUC comparison of traditional forensic detectors across nine RA-Bench generative subsets

Generator	FLIP	MVP	LCDF	DTD	FFDN	TruFor	CDFA	Effort	DRCT	F.MoE	Ours V1	Ours V2
Happy Horse-1.1	40.0	46.9	55.5	50.0	55.9	49.9	53.6	43.9	71.5	50.2	53.9	96.3
Hunyuan Video-1.5	59.6	44.8	55.1	50.0	49.6	49.6	54.6	48.0	64.1	51.5	50.3	95.9
JoyAI- Echo	39.4	33.8	50.2	50.0	53.1	50.7	52.1	50.2	57.2	51.9	51.9	96.5
KlingV3 Video	51.6	55.7	63.1	50.0	42.6	49.3	60.9	54.3	48.4	51.6	56.2	96.1
KlingV3 Omni	46.9	66.2	63.6	50.0	43.8	49.1	61.7	55.0	47.1	52.1	56.2	96.3
LongCat Video	50.6	58.0	57.1	50.0	57.2	49.4	55.2	49.6	62.2	58.1	58.7	95.6

Table 16: Video-level balanced accuracy comparison of traditional forensic detectors across eleven RRVBench generative subsets

Document Image Manipulation Localization Performance (Pixel-F1)
Method	DTD	CAFTB	TIFDM	DefakerOne	DefakerOne2
DocTamperFCD	68.6	29.2	9.0	80.8	86.9
DocTamperSCD	73.9	37.7	25.7	74.9	83.5
DocTamperTest	80.3	32.8	25.9	73.3	79.6
Tampered IC13	83.4	83.9	79.7	90.5	92.1
T-SROIE	92.1	91.7	89.4	81.7	84.6

Table 18: Pixel-level forgery localization performance comparison under binary F1 metric. The upper block reports document benchmarks with document-oriented baselines, and the lower block reports nature benchmarks with nature-oriented baselines

Domain	DTD	FFDN	Trufor	CDFA	Effort	DRCT	F.MoE	Ivy-xDetector	Veritas	Ours V1	Ours V2
Image Domain
DeepFake	36.8	57.4	53.4	87.9	88.2	62.4	40.9	74.0	13.6	95.8	96.7
AIGC	44.7	49.5	46.2	49.8	66.1	68.2	57.7	79.9	72.2	87.5	93.3
Document	59.1	56.6	58.5	47.8	41.7	58.8	42.0	40.2	47.7	81.4	87.6
Nature	52.5	34.8	71.5	64.7	63.4	62.5	25.6	51.8	2.0	86.7	89.6
Physical	32.0	60.0	34.2	39.3	67.6	44.6	31.9	35.5	51.2	50.7	87.2

Table 19: Domain-wise summary of full-spectrum forgery detection. ‘Physical’ denotes average accuracy on CASIA-FASD, DLC2021 and SCVD. ‘Digital’ denotes average accuracy on GenBuster-ID, GenBuster-OOD, GenBuster++, LOKI, VideoPhy2.

Settings	All-LoRA	All-Full	Ours (Stage 1)	Ours (Stage 2)	Ours (Stage 3)
Image Average	87.3	87.8	84.6	88.4	90.9
Video Average	93.6	93.0	91.8	94.2	95.7

Table 20: Ablation of the proposed Tide-Style Tuning. Image/video-level detection task

Acc. / Ratio	Frame Acc.	Video Acc.	Frame Ratio	Video Ratio	Ours Acc.
CASIA-FASD	90.0	89.8	61.5%	38.5%	90.3
DLC2021	99.9	99.7	95.4%	4.6%	99.9
GenBuster++	72.2	88.4	23.7%	76.3%	88.6
GenBuster ID	99.9	99.9	92.4%	7.6%	99.9
GenBuster OOD	97.2	98.5	23.5%	76.5%	99.6
RABench	98.2	87.4	44.0%	56.0%	96.3

Table 21: Ablation of the proposed Modality Synergism. Image/video-level detection task

Settings	temperature=1, seed=			seed=42, temperature=
Settings	42	1024	4096	0	0.5	1
Image Average	90.9	90.8	91.0	90.9	91.0	90.9
Video Average	95.7	95.7	95.6	95.7	95.5	95.7

Table 22: Ablation study of decoding hyperparameters. Image/video-level detection task

Model	DefakerOne			DefakerOne2
Dataset/ $\Delta$	MFFI	MFFI-Distorted	$\Delta$	MFFI	MFFI-Distorted	$\Delta$
AUC	96.1	76.4	-20.5%	97.3	90.1	-7.4%

Table 23: Robustness evaluation against distortions on MFFI dataset under AUC metric. Image-level detection task

Document Image Manipulation Localization Performance (Pixel-F1)
Setting	(a) Baseline	(b) +A.M. (Conv)	(c) +A.M. (DINO)	(d) DefakerOne2
DocTamperFCD	59.7	76.1	86.7	86.9
DocTamperSCD	34.6	68.4	83.4	83.5
DocTamperTest	41.9	73.9	79.3	79.6
Tampered IC13	85.8	91.6	92.8	92.1
T-SROIE	88.2	89.7	87.2	84.6

Table 24: Ablation study of the proposed Anomaly Magnifier and MLLM Calibration. Pixel-level forgery localization task under binary F1 metric. The upper block reports document benchmarks and the lower block reports nature benchmarks

Figure 7: t-SNE projection of the last-layer hidden states preceding the LLM classification head ( $2{,}396$ samples, $2{,}048$ dimensions, eight flags). All points are randomly sampled from the test sets, with no filtering by prediction. Marker shape denotes the forensic flag; marker color denotes the ground-truth label and prediction correctness (blue: fake and correct; pink: real and correct; yellow: real but wrong; red: fake but wrong). The five annotated manifolds C0–C4 are quantified in Table 25 .

Cluster	aigc	deepfake	doc	frameaigc	All
C0	15 (4.1%, 86.7%)	–	11 (4.6%, 63.6%)	71 (14.1%, 98.6%)	114 (4.8%, 93.0%)
C1	204 (55.4%, 78.4%)	278 (100.0%, 91.7%)	135 (56.0%, 94.1%)	36 (7.2%, 97.2%)	834 (34.8%, 86.7%)
C2	24 (6.5%, 91.7%)	–	34 (14.1%, 94.1%)	88 (17.5%, 88.6%)	828 (34.6%, 90.0%)
C3	94 (25.5%, 100.0%)	–	42 (17.4%, 92.9%)	277 (55.1%, 98.2%)	492 (20.5%, 95.5%)
C4	31 (8.4%, 77.4%)	–	19 (7.9%, 89.5%)	31 (6.2%, 100.0%)	128 (5.3%, 85.2%)
All	368 (100.0%, 85.1%)	278 (100.0%, 91.7%)	241 (100.0%, 92.1%)	503 (100.0%, 96.6%)	2,396 (100.0%, 89.9%)

Table 25: Per-cluster statistics of the t-SNE projection in Figure 7 , over

2{,}396

inputs randomly sampled from the eight test sets ( frameaigc / framespoof : frames cut with Ada-FPS from AI-generated videos / from physical presentation-attack videos; videoaigc / videospoof : the same two video families under the native video modality). The two sub-tables split the eight flags into the image-derived ones (a) and the video-derived ones (b); both share the same five clusters. Each cell reports the number of samples of that flag falling into the cluster, its share of all sampled instances of the flag (in %), and the detection accuracy within the cell (in %), i.e. count (share%, acc%) ; the dominant cluster of each flag is bolded, and “–” denotes an empty cell. The bottom row of each sub-table aggregates over all clusters, and the All column reports the cluster size together with the overall cluster accuracy.

Figure 8: Per-type view of the t-SNE projection of Figure 7 : the identical embedding ( $2{,}396$ randomly sampled test inputs, $2{,}048$ dimensions) re-plotted once per forensic flag, with each panel showing only the samples of that flag ( $n$ as titled; marker colors as in Figure 7 ).

Setting	Pre-trained Model	FPR	In-house Test Data1	In-house Test Data2	In-house Test Data3	In-house Test Data4	Avg1	Avg2
(a)	DefakerOne (Ours V1)	0.001	94.11	86.12	81.12	-	87.12	-
(b)	InternVL2-2B	0.001	87.37	73.33	85.94	-	82.21	-
(c)	Qwen3.5-2B	0.001	93.93	87.54	92.77	-	91.41	-
(d)	SwinTrans.	0.001	95.13	81.63	72.59	15.66	83.12	66.25
(e)	DINO-MAC	0.001	93.92	90.47	92.78	79.86	92.39	89.26
(f)	DefakerOne2-0.8B (Ours V2)	0.001	94.92	94.19	95.58	80.61	94.90	91.33

Table 26: Evaluation results on in-house test datasets. Pre-trained models are fine-tuned on the data in-house training data. Compared models include DefakerOne Team (2026) , InternVL2 Chen et al. (2024g) , Qwen3.5-2B Qwen3.5 Team (2026) , SwinTransformer (SwinTrans.) Liu et al. (2021b) , DINO-MAC Qu et al. (2026a) that fine-tuned on the same in-house data.

Image Forgery Detection

Image Forgery Detection studies how to identify manipulated or synthetic images and locate the visual evidence of tampering.

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May 13, 2026J/K move · Enter open · S save

Venus-DeFakerOne: Unified Fake Image Detection & Localization

GuangJian Team

Ant Group

Image Forgery Detection Digital Image Forensics Image Forgery Localization AI-Generated Image Detection

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In recent years, the rapid evolution of generative AI has fundamentally reshaped the paradigm of image forgery, breaking the traditional boundaries between document editing, natural image manipulation, DeepFake generation, and full-image AIGC synthesis. Despite this shift toward unified forgery generation, existing research in Fake Image Detection and Localization (FIDL) remains fragmented. This creates a mismatch between increasingly unified forgery generation mechanisms and the domain-specific detection paradigm. Bridging this mismatch poses two key challenges for FIDL: understanding cross-domain artifacts transfer and interference, and building a high-capacity unified foundation model for joint detection and localization. To address these challenges, we propose DeFakerOne, a data-centric, unified FIDL foundation model integrating InternVL2 and SAM2. DeFakerOne enables simultaneous image-level detection and pixel-level forgery localization across diverse scenarios. Extensive experiments demonstrate that DeFakerOne achieves state-of-the-art performance, outperforming baselines on 39 forgery detection benchmarks and 9 localization benchmarks. Furthermore, the model exhibits superior robustness against real-world perturbations and state-of-the-art generators such as GPT-Image-2. Finally, we provide a systematic analysis of data scaling laws, cross-domain artifacts transfer-interference patterns, the necessity of fine-grained supervision, and the original resolution artifacts preservation, highlighting the design principles for scalable, robust, and unified FIDL.

Abstract

Image Forgery Detection

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