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This paper presents a new approach to defining additive steganographic distortion in the spatial domain. The change in the output of directional high-pass filters after changing one pixel is weighted and then aggregated using the reciprocal Hölder norm to define the individual pixel costs. In contrast to other adaptive embedding schemes, the aggregation rule is designed to force the embedding changes to highly textured or noisy regions and to avoid clean edges. Consequently, the new embedding scheme appears markedly more resistant to steganalysis using rich models. The actual embedding algorithm is realized using syndrome-trellis codes to minimize the expected distortion for a given payload.