With the explosion of information stored world-wide, data intensive computing has emerged as a central area of research. Efficient management and processing of this massively exponential amount of data from diverse sources, such as telecommunication call data records, telescope imagery, online transaction records, web pages, stock markets, medical records (monitoring critical health conditions of patients), climate warning systems, etc., has become a necessity. Removing redundancy from such huge (multi-billion records) datasets results in resource and compute efficiency for downstream processing and constitutes an important area of study. "Intelligent compression" or deduplication in streaming scenarios, for precise identification and elimination of duplicates from the unbounded data stream is a greater challenge given the real-time nature of data arrival. Stable Bloom Filters (SBF)  address this problem to a certain extent. However, SBF suffers from a high false negative rate and slow convergence rate, thereby rendering it inefficient for applications with low false negative rate tolerance. In this paper, we present a novel reservoir sampling based Bloom filter (RSBF) technique, based on the combined concepts of reservoir sampling and Bloom filters for approximate detection of duplicates in data streams. Using detailed theoretical analysis we prove analytical bounds on its false positive rate, false negative rate and convergence rates with low memory requirements. We show that RSBF outperforms SBF in terms of false negative rates and convergence rates while consuming the same amount of memory. Using empirical analysis on real-world datasets (3 million records) and synthetic datasets with around 1 billion records, we demonstrate upto 2x improvement in false negative rate with better convergence rates as compared to SBF, while maintaining comparable false positive rates. To the best of our knowledge, this is the first attempt to integrate reservoir sampling method with Bloom filters for deduplication in streaming scenarios. Copyright 2012 ACM.