Gradient Masking in Adversarial Training and Explainability

Validate core concepts, prototype SCOR‑PIO 2.0, SGAM, and PGCA in isolation, and establish baseline metrics.

Steps

• Literature & Threat Model Review(3 wks)
  Map attack surface, define robustness and explainability metrics, and select benchmark datasets.
• SCOR‑PIO 2.0 Prototype(4 wks)
  Implement HVP‑based curvature regularizer using Pearlmutter’s trick and evaluate on ResNet‑50.
• SGAM Masking Layer(3 wks)
  Build lightweight attention module, integrate Grad‑CAM++ approximation, and test mask fidelity.
• PGCA Attribution Module(3 wks)
  Develop perturbation‑gradient consensus pipeline and benchmark faithfulness scores.
• Baseline Integration & Logging(2 wks)
  Combine modules into a single training loop, add audit logging for mask generation.

Milestones

Risks

• HVP computation may become a bottleneck on large models
• Masking may inadvertently remove salient features, hurting accuracy

Scale FGMF to vision transformers and multi‑agent simulation, and expose APIs for downstream services.

Steps

• Transformer Integration(4 wks)
  Adapt SCOR‑PIO 2.0 and SGAM to ViT‑base, ensuring memory‑efficient HVPs.
• Multi‑Agent Coordination Layer(4 wks)
  Implement shared saliency aggregation and consensus across agents.
• API Design & Containerization(2 wks)
  Expose training and inference endpoints via gRPC, package into Docker images.
• Performance Benchmarking(2 wks)
  Measure latency, GPU utilization, and memory footprint on edge‑GPU targets.

Milestones

Risks

• Transformer HVPs may exceed GPU memory limits
• Cross‑agent consensus may introduce synchronization overhead

Dependencies

• Phase 1 baseline modules
• Availability of ViT‑base weights

Validate FGMF against a comprehensive attack suite, quantify explainability fidelity, and ensure auditability compliance.

Steps

• Adaptive Attack Evaluation(4 wks)
  Run AutoAttack, PGD‑20, and CW‑L2 on both CNN and ViT models.
• Explainability Audit(3 wks)
  Compare PGCA consensus maps against ground‑truth saliency (e.g., ImageNet‑Saliency dataset).
• Mask Logging & Traceability(2 wks)
  Implement immutable log of SGAM masks, integrate with blockchain‑based audit ledger.
• Regulatory Gap Analysis(1 wk)
  Map audit logs to GDPR/HIPAA requirements and produce compliance report.

Milestones

Risks

• Attack suite may uncover hidden gradient masking collapse
• Audit ledger integration may introduce latency
• Regulatory requirements may evolve during testing

Dependencies

• Phase 2 prototype APIs
• Access to attack libraries and benchmark data

Deploy FGMF in a controlled multi‑agent environment (e.g., autonomous vehicle simulation) and collect real‑world performance data.

Steps

• Simulation Environment Setup(2 wks)
  Integrate FGMF into CARLA or AirSim for multi‑agent perception tasks.
• Real‑World Data Collection(2 wks)
  Gather sensor streams, run inference, and log explanations and masks.
• Human‑in‑the‑Loop Evaluation(1 wk)
  Conduct usability studies with operators reviewing saliency maps and audit trails.

Milestones

Risks

• Simulation‑to‑real gap may reveal unseen robustness issues
• Operator fatigue may bias usability results

Dependencies

• Phase 3 validated models
• Access to simulation platform

Migrate FGMF to production infrastructure, establish monitoring, and finalize regulatory approvals.

Steps

• Infrastructure Provisioning(2 wks)
  Deploy containerized services on Kubernetes with GPU autoscaling.
• Continuous Monitoring(2 wks)
  Set up dashboards for robustness metrics, explanation quality, and audit log integrity.
• Regulatory Approval(2 wks)
  Submit final compliance package to relevant authorities (FDA, NHTSA, etc.).

Milestones

Risks

• Production scaling may expose hidden memory leaks
• Regulatory changes could delay approval

Dependencies

• Phase 4 pilot data
• Operational Kubernetes environment