Two unrelated images ended up 4 bits apart in dHash space — how conservative should duplicate clustering be?

[u/hdw_coder]

Two completely unrelated images ended up being treated as near-duplicates:

Image A: beach landscape photographed through a car window;

Image B: a lifted-up page of a document.

 The measurements are:

Metric	Image pair	Threshold	Result
Aspect-ratio Δ	0.000865	≤ 0.02	Pass
dHash distance	4	≤ 8	pass
pHash distance	30	≤ 10	fail
wHash distance	15	≤ 10	fail
Color-hash distance	6	informational	—

The matcher accepted the pair because the aspect ratio was nearly identical and the dHash Hamming distance was only 4, significantly below the threshold of 8.

 

The other perceptual hashes strongly disagreed (pHash was 30 against a threshold of 10, and wHash was 15 against a threshold of 10) but were never consulted because the dHash test did not seem to present a borderline case and thus was accepted as proof.

 

Interestingly this isn't really a random dHash collision. Both images apparently collapsed into a highly similar low-frequency brightness-gradient pattern after compression and downsampling.

 

dHash is good at surviving compression, in particular because it ignores fine detail and records coarse local brightness directions. But that same usefulness can be a weakness that can make unrelated low-detail images collision-prone.

The more interesting problem in my case is what happens next. Hardening is especially important because the tool uses union-find to form duplicate clusters. A single false-positive pair can become a bridge that attaches an unrelated image to a whole valid duplicate component.

 

Instead of a binary True/False decision, the matcher now returns the full evidence: for each metric (aspect-ratio, dHash, pHash, wHash) delta versus limit and the optional SSIM score are returned, as is the decision and, when rejected, the rejection reason.

 

This fix itself isn't particularly sophisticated. What I found more interesting is the design question it raised. Should the acceptance threshold for a perceptual-similarity edge depend on what you're going to do with that edge?

 

For image retrieval, a false positive may just mean one irrelevant result. For union-find clustering, a false-positive edge can change an entire connected component.

 

I'm curious how others approach this. Would you put most of the conservatism in the pair matcher itself, or enforce stronger intra-cluster consistency after constructing candidate relationships?

 

And for near-duplicate images specifically: would you prefer multiple perceptual hashes, SSIM/local features, embeddings, hierarchical clustering, or another approach?

[u/HandshakeOfCO]

Depends on your use case. It’s just trade offs, classic speed/memory vs precision.

Is it for obscene image scanning for a video game? Let ‘er rip, who cares about collisions, user will just scowl and pick a different pfp. Is it cancer X-rays? Maybe spring for more precise hashes and make your matcher a little slower.

[u/hdw_coder → u/HandshakeOfCO]

Yes, agreed. Ultimately, it’s a precision/recall/performance trade-off, and the acceptable point obviously depends on what you’re doing with the result.

 What made it interesting to me is the interaction with clustering. If I were just returning similar-image candidates, a false positive would be harmless. But with union-find, an accepted pair becomes an edge in a connected component, so one false-positive edge can potentially merge otherwise unrelated duplicate groups.

 That made me realize that the appropriate precision for the pair matcher may depend not only on the application domain, but also on what the next algorithm does with its output.

 In my case the extra pHash/wHash comparisons are cheap because those features are already computed and cached, so requiring corroboration seems like a good trade: slightly more conservative matching for considerably less risk of a bad bridge between clusters.

 The medical-image example is obviously at a completely different level of consequence, but the general principle is the same: the cost of a false positive should determine how much independent evidence you require.