Comprehensive AIGI Detection Benchmark
Is Artificial Intelligence Generated Image Detection a Solved Problem?
AIGIBench evaluates state-of-the-art detectors across real-world challenges, revealing significant performance gaps and guiding future research.
Executive Impact: Unveiling the Gaps in AI Detection
Despite high reported accuracies, current AIGI detectors struggle in real-world scenarios. AIGIBench highlights critical areas for improvement.
79.9%
Highest Overall Accuracy (SAFE) in Setting-II (Table 3)
0%
F.Acc. under JPEG Compression
11
Advanced Detectors Evaluated
23
Diverse Fake Image Subsets
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Evaluates detector performance across diverse generative models and datasets, including unseen sources and real-world manipulations. Reveals significant performance disparities and the need for more robust, generalizable detectors.
79.9%
Highest Overall Accuracy (SAFE) in Setting-II (Table 3)
| Challenge | Impact | AIGIBench Finding |
|---|---|---|
| Multi-Source Generalization | Detectors struggle with images from unknown generative models. |
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| Deepfake & In-the-Wild | Significant performance degradation on DeepFake variants and social media content. |
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| Training Data Diversity | Limited training data leads to overfitting. |
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Enterprise Process Flow
Training Phase
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Data Augmentation
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Pre-processing
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Detection
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Binary Classification (Real/AIGI)
Examines detector resilience to various image degradations (JPEG compression, Gaussian noise, up-down sampling). Highlights critical vulnerabilities, especially under JPEG compression, where F.Acc. approaches 0%.
0%
F.Acc. under JPEG Compression for many detectors (Table 4)
Impact of Image Degradation
AIGIBench's robust evaluation reveals that many detectors, particularly older CNN-based ones, fail catastrophically under common image degradations like JPEG compression and Gaussian noise. This indicates a significant gap between reported lab performance and real-world applicability. Newer models like AIDE and DFFreq show better resilience but still face challenges in maintaining high fake image accuracy (F.Acc.). This suggests that current methods are biased towards classifying degraded images as 'real', severely compromising detection reliability.
Analyzes the impact of different data augmentation strategies (rotation, color jitter, masking) on detector performance. Findings suggest limited benefits and potential trade-offs, emphasizing the need for model-dependent augmentation pipelines.
Limited
Benefits from Common Augmentations (Table 5)
| Augmentation Type | Impact on R.Acc. | Impact on F.Acc. | Overall Finding |
|---|---|---|---|
| Rotation |
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| Color Jitter |
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| Masking |
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| Combined Augmentations |
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Investigates the effectiveness of cropping vs. resizing during inference. Reveals that cropping primarily boosts real image accuracy (R.Acc.) but offers limited or negative impact on fake image accuracy (F.Acc.) due to modality asymmetry.
R.Acc. Boost
Primary benefit of 'Crop' pre-processing (Table 6)
Enterprise Process Flow
Test Image
→
Pre-processing (Crop/Resize)
→
Detector Input
→
AIGI Detection
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Authenticity Output
Pre-processing Modality Asymmetry
Our analysis reveals a critical modality asymmetry in pre-processing. While 'Crop' improves R.Acc. by preserving high-frequency details in real images, it offers inconsistent or even negative impacts on F.Acc. for fake images. This is because real images have consistent local structures, which Crop enhances, but synthetic images have diverse and often subtle generative artifacts, which Crop might not consistently highlight or could even remove. This highlights a fundamental challenge in designing universal pre-processing strategies for AIGI detection.
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Your AI Implementation Roadmap
A structured approach to integrating cutting-edge AI detection into your enterprise.
Phase 1: Discovery & Assessment
Comprehensive audit of existing systems and data, defining success metrics and identifying high-impact use cases.
Phase 2: Pilot & Proof-of-Concept
Develop and deploy a small-scale pilot project using AIGIBench-validated detectors, establishing initial ROI.
Phase 3: Scaled Integration & Optimization
Full integration across enterprise systems, continuous monitoring, and performance tuning.
Phase 4: Advanced AI Resilience
Implement continuous learning loops and adaptive strategies to counter evolving AI generation techniques and degradations.
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