In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach that is able to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use the fact that most robust hashing algorithms are based on dimensionality reduction using random projections and quantization. Therefore, we demonstrate the corresponding achievable rate and probability of error based on random projections and compare with the results for the direct domain. The effect of dimensionality reduction is studied and the corresponding approximations are provided based on the Johnson-Lindenstrauss lemma. Side-information assisted robust perceptual hashing is proposed as a solution to the above shortcomings.