Recently Zhang et al. described an algorithm for the detection of ±1 LSB steganography based on the statistics of the amplitudes of local extrema in the grey-level histogram. Experimental results demonstrated performance comparable or superior to other state-of-the-art algorithms. In this paper, we describe improvements to this algorithm to (i) reduce the noise associated with border effects in the histogram,and(ii)extendthe analysisto amplitudesof localextremainthe2D adjacencyhistogram. The new algorithm, using 10 features derived from the 1D and 2D histograms, also significantly outperforms other state-of-the-art steganalyzers.