Project ASTRA: Real-Time Activation Subspace Deflection for Deep Neural Networks via Float64 Nullspace Projection

Deep Neural Networks (DNNs) exhibit acute vulnerabilities to intermediate activation layer perturbations engineered through out-of-distribution (OOD) noise injection and feature-steering gradient updates. Conventional defensive paradigms—such as adversarial retraining or external pre-processing guardrails—introduce severe computational latency penalties (≥10 ms) and cause clean distribution performance degradation. This paper presents Project ASTRA (Zero-retraining Vector Interception Layer v2.0), a parameter-invariant runtime framework designed to dynamically intercept high-dimensional 4D activation tensors (B×C×H×W) and deflect adversarial noise into an orthogonal complement nullspace manifold. Constructed via singular value decomposition (SVD) across uncorrupted baseline calibration maps, the rank-K projection operator P_|| = U_K U_K^T attenuates non-subspace noise components without modifying underlying neural parameters. Executed under strict IEEE 754 Float64 double-precision numerical boundaries, the interception hook bounds the residual noise floor to ≤10^-12. Empirical evaluation over 10^6 continuous verification cycles demonstrates a runtime SLA overhead of +0.42 ms per batch, establishing an efficient, mathematically bounded defense layer for real-time inference pipelines.