🐢 📷 Giskard-Vision: Automated Vulnerability Scanning for Computer Vision Models (Python Practice) As computer vision systems deploy into high-stakes domains—from medical diagnostics to autonomous vehicles—single-metric evaluations rarely capture the full risk landscape. Giskard-Vision brings systematic, automated scanning to CV models, layering metamorphic tests, bias detectors, and robustness probes into a unified, reproducible workflow. Whether you're auditing a skin cancer classifier or stress-testing an object detector, Giskard-Vision turns ad-hoc validation into auditable, scalable assurance. 🧠 Core Concept - Task-Agnostic Wrappers: Wrap any PyTorch/TensorFlow model and dataset with lightweight adapters (SkinCancerHFModel, DataLoaderSkinCancer) to enable plug-and-play scanning across image classification, object detection, and landmark tasks. - Automated Vulnerability Discovery: The scan() function automatically probes for performance degradation, fairness biases, stochastic unrobustness, and ethical edge cases—no manual test design required. - Metamorphic Testing Engine: Leverages invariant-based transformations (rotation, blur, lighting shifts) to verify model consistency under semantically-preserving perturbations. - Actionable HTML Reports: Results render as interactive dashboards highlighting problematic data slices, sample images, and severity-ranked issues—enabling targeted model refinement. 💻 Implementation Example from giskard_vision.image_classification.models.wrappers import SkinCancerHFModel from giskard_vision.image_classification.dataloaders.loaders import DataLoaderSkinCancer from giskard_vision import scan # Step 1: Wrap your model & dataset (pre-built wrappers available for HF models) model = SkinCancerHFModel() dataset = DataLoaderSkinCancer() # Step 2: Run automated scan (num_images limits scope for quick iteration) results = scan(model=model, dataset=dataset, num_images=5, raise_exceptions=True) # Step 3: Review interactive HTML report (auto-generated in notebook output) # → Highlights bias slices (e.g., "underperforms on elderly demographics") # → Shows metamorphic failure cases (e.g., "prediction flips after 15° rotation") # → Exports findings for CI/CD gating or stakeholder review 🔥 Use Cases - Medical CV Auditing: Detect demographic biases in diagnostic models before clinical deployment—e.g., flagging reduced sensitivity on underrepresented skin tones. - Robustness Benchmarking: Systematically compare model resilience to corruptions (noise, occlusion, weather sim) using standardized metamorphic suites. - Bias Mitigation Workflows: Integrate scan results into retraining loops to prioritize data augmentation on vulnerable slices identified by the scanner. - Regulatory Readiness: Generate auditable evidence of fairness/robustness testing for compliance frameworks (EU AI Act, FDA SaMD guidelines). ⚠️ Caveats & Responsible Practice - Wrapper Fidelity: Custom model wrappers must correctly expose prediction interfaces. Validate wrapper behavior on sanity checks before scanning to avoid false negatives - Scan Scope vs. Cost: Full-dataset scans can be compute-intensive. Use num_images subsampling for iteration, but validate critical findings on representative holdout sets - Contextual Interpretation: Automated flags require human review. A "bias detection" on age groups may reflect dataset imbalance—not model fault. Triangulate with domain expertise. - Adversarial Gap: Metamorphic tests complement—but don't replace—dedicated adversarial attack evaluation. Layer Giskard-Vision with tools like ART or CleverHans for defense-in-depth. 🔗 Resources - Documentation #GiskardVision #ComputerVision #AISafety #ModelTesting