We present a two-layer construction for image hashing. First, a \emph{perceptual} binary code $c(x)$ is derived from a ResNet-18 embedding (after global average pooling, $d=512$) via a linear projection and sign quantization; optionally, a real-valued serialization of length $n=8ds$ bits is used. The code $c(x)$ enables fast approximate nearest-neighbor search: we empirically measure robustness to permissible transforms (low intra-BER), separability of unrelated pairs (inter distances near $n/2$), bit balance and weak inter-bit correlations, and we estimate a lower bound on the source min-entropy. Second, $c(x)$ serves as a noisy source for a \emph{fuzzy extractor} producing a reproducible secret $R$ and public data $P$; a cryptographic tag $T$ is then derived via KDF and HMAC/SHA-3. This preserves similarity search over $c(x)$ while assigning cryptographic guarantees (preimage/second-preimage/collision) to $T$, which reduce to the security of the underlying primitives given sufficient post-publication min-entropy $H_\infty(C,|,P)$. We discuss limitations of perceptual hashes (adversarial examples) and parameter selection ($n$, error-correction radius $t$, secret length $|R|$) driven by measured BER distributions and min-entropy estimates.