Assuming that watermarking is feasible (say, against a limited set of attacks of significant interest), current methods use
a secret key to generate and embed a watermark. However, if the same key is used to watermark different items, then each instance
may leak partial information and it is possible that one may extract the whole secret from a collection of watermarked items.
Thus it will be ideal to derive content dependent keys, using a
perceptual hashing algorithm (with its own secret key) that is resistant to small changes and otherwise having randomness and unpredictability
properties analogous to cryptographic MACs.
The techniques here are also useful for synchronizing in streams to find fixed locations against insertion and deletion attacks. Say, one may watermark a frame in a stream and
can synchronize oneself to that frame using keyed perceptual hash and a known value for that frame. Our techniques can be
used for identification of audio clips as well as database lookups in a way resistant to formatting and compression. We propose a novel audio hashing algorithm to be used for audio watermarking
applications, that uses signal processing and traditional algorithmic analysis (against an adversary).