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Total Tests Executed
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Anomalies Discovered
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Multi-Model Defeats (2+)
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Tests/Min (Last Hour)
1. Pattern Discovery: What Works Best
Why This Matters
Identifying the most effective adversarial patterns reveals fundamental vulnerabilities in facial recognition systems. Patterns with high success rates demonstrate reproducible attack vectors that current AI models consistently fail to handle. This data is critical for both defensive improvements and understanding the limits of current technology.
Top Performing Patterns by Success Rate
2. Model-Specific Vulnerabilities
Why This Matters
Each test runs through a 10-model "gauntlet" simulating real-world surveillance: Person Detectors (P1-P4: YOLOv8n, YOLOv5s, SSD-MobileNetV2, ResNet34-SSD), Face Detectors (F1-F4: InsightFace Buffalo_L, FaceNet, MTCNN, RetinaFace), and Face Recognizers (R1-R2: ArcFace, FaceNet). Understanding which patterns defeat which models reveals architectural blind spots in specific neural network designs.
Pattern Effectiveness Across AI Models
Overall Model Vulnerability Comparison
3. Multi-Model Defeats: Universal Patterns
Why This Matters
Multi-model anomalies are scored by severity: PRIORITY (2+ models defeated), EXTREME (any P-model + any F-model), PERSON_STEALTH (all P1-P4 defeated, +400pts), FACE_STEALTH (all F1-F4 defeated, +400pts), and TOTAL_STEALTH (all P and F models defeated, +1000pts). These patterns represent fundamental flaws in computer vision that transcend specific model architectures.
Patterns Defeating 3+ Models Simultaneously
4. Pattern Categories: Attack Taxonomy
Why This Matters
Adversarial patterns fall into distinct categories: Geometric (lines, checkerboards), Noise (random perturbations), Semantic (facial features, art), and Camouflage (environmental textures). Understanding which categories work best informs both offensive research (which attack types to prioritize) and defensive strategies (which visual features to make models robust against).
Effectiveness by Pattern Category
Anomaly Type Distribution
5. Target Vulnerabilities: Persona Analysis
Why This Matters
Not all faces are equally recognizable. Some test subjects (personas) are more vulnerable to adversarial patterns than others. This reveals potential demographic biases, the impact of facial characteristics (skin tone, bone structure, facial hair), and pose/lighting conditions that make recognition harder. Understanding these differences is crucial for fairness and robustness in facial recognition deployment.
Image Vulnerability Rankings
Test Distribution by Persona
6. Pattern Synergies: Combination Effects
Why This Matters
Some patterns work better together than alone. Our fuzzer applies 1-3 layered patterns per test. Synergy analysis reveals which combinations amplify each other's effectiveness. For example, a noise base layer might enhance a geometric overlay. These insights guide the genetic algorithm's crossover operations and help identify non-linear interactions in adversarial space.
Top Pattern Combinations in Successful Attacks
7. Statistical Rigor: Success vs. Sample Size
Why This Matters
A pattern with 100% success rate but only 2 tests is not statistically significant. This scatter plot correlates success rate with test volume to identify which high-performing patterns have sufficient sample sizes for confident conclusions. Patterns in the top-right quadrant (high success + high volume) are the most reliable findings. This analysis prevents overfitting to lucky outliers.
Pattern Success Rate vs. Test Volume
8. Research Progress: Discovery Over Time
Why This Matters
The time series shows our research velocity and whether we're still discovering new vulnerabilities. A steep curve indicates rapid discovery, while a plateau suggests we've exhausted the current search space. This informs whether we need new pattern categories, different test subjects, or alternative fuzzing strategies. It also demonstrates the value of the distributed testing network.
Tests Per Minute (Last 5 Days)
Current Rate
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tests/min (last hour)
Daily Projection
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tests/day at current rate
Goal Progress
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% of 8.3M tests/day target
Cumulative Anomalies Discovered Over Time
9. Distributed Network: Contributor Analysis
Why This Matters
This research is powered by a distributed network of volunteer workers. Understanding contributor distribution validates our distributed architecture's effectiveness and acknowledges top contributors. It also reveals whether findings are concentrated among a few nodes (potential bias) or distributed broadly (better generalization). Network health is crucial for long-term research sustainability.
Top Contributors by Test Volume
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