Iwf talks about usage of grid image for hash collisions, waht is being down to prevent thsi
Executive summary
The “grid image” approach in perceptual hashing—where an image is mapped to grid-based gradient or cell summaries—creates structured, low-dimensional fingerprints that can produce unintended hash collisions; researchers analyzing NeuralHash and PhotoDNA have shown how such grid-derived descriptors yield repeatable collision vectors and stress the need for robustness [1]. Defenses being deployed or proposed include multiple independent hash outputs or sign-bit hashing, randomized/salted hash keys, threshold tuning with human review, rate limiting and input-size caps, and algorithmic hardening—each of which addresses different collision vectors but also brings tradeoffs in performance, privacy, and false-positive risk [2] [1] [3].
1. How grid-based perceptual hashes create collision risk
Perceptual hashes that reduce an image to gradients or averaged cell values on a tiling—what reporting calls a “grid image”—compress visual detail into a compact vector; those grid values are the basis of comparisons, and if the mapping or quantization is coarse, distinct images can map to the same or close vectors, producing collisions or near-collisions that trigger false matches [1]. The technical literature that reverse‑engineered Apple’s NeuralHash and Microsoft’s PhotoDNA shows the suspected process built on small sets of gradient values per grid cell—e.g., 36 grids producing 144 gradient values in one analysis—which concentrates a lot of representational weight into a small structure and makes deliberate or accidental collisions easier to find [1].
2. Cryptographic and algorithmic mitigations: randomization, salting, and multiple hashes
A core mitigation is to reduce predictability in the hash pipeline: adding keyed randomness or “salting” of the hashing steps and using more than one independent hash function raises the bar for an adversary to craft collisions, because attackers must now solve for multiple coordinated outputs rather than a single deterministic mapping [1]. In feature‑hashing literature, practitioners recommend a second, single‑bit hash to determine sign or other orthogonal outputs to counter collision effects—an inexpensive, complementary defense that reduces systematic cancellation or accumulation artifacts from collisions [2]. Historically, when hash collisions threatened web frameworks, platforms introduced hash randomization to make collision attacks nonrepeatable—an applied precedent for perceptual hashers [3].
3. Operational controls: thresholds, human review, and rate limits
Where algorithmic perfectness cannot be achieved without unacceptable false positives, systems rely on operational controls: tuning similarity thresholds to trade precision for recall, routing borderline matches to human analysts for verification, and applying rate limits or caps on submission size to prevent mass probing that seeks collisions [1] [3]. Security reporting on hash-table collision DDoS also recommends limiting maximum POST sizes to reduce the combinatorial space attackers can probe for collisions—an operational countermeasure equally relevant to large-scale perceptual-hash scanning deployments [3].
4. Tradeoffs, measurement, and the need for transparency
Every mitigation has costs: stronger randomness and multiple hashes increase computation and complicate client‑side deployments; lower thresholds and heavier human review raise privacy and scalability questions; and aggressive rate limits can hinder legitimate detection workflows [1] [2]. The ePrint analysis of NeuralHash and PhotoDNA cautions that internal, proprietary choices matter—a lack of public specs blocks external validation of collision resistance and bit‑distribution uniformity, which are crucial properties to prevent information leakage and adversarial targeting [1].
5. What remains unclear from available reporting
Public technical analyses document vulnerabilities and suggest defenses, but they cannot confirm the exact internal mechanisms, parameters, or operational practices of commercial or government deployments without access to source implementations or disclosure; researchers explicitly note their descriptions are high level and not fully verifiable against closed systems [1]. Consequently, it is not possible from the provided reporting to declare which vendors have adopted which precise mitigations, or to quantify residual collision risk after those mitigations are applied [1].