![]() ![]() The method of claim 6, wherein the wavelet transform is a Haar wavelet transform. The method of claim 3, wherein the wavelet transform is at least a 1-level discrete wavelet transform. The method of claim 3, wherein the discrete wavelet transform is one of a 1, 2 or 3 level wavelet transform. ![]() The method of claim 3, wherein the discrete wavelet transform is at least one of a Daubechies 9/7 wavelet and an integer 5/3 wavelet. The method of claim 1, further comprising computing a discrete wavelet transform for at least one of the spatial representation of the image, the BDCT coefficient 2-D arrays, the JPEG coefficient 2-D arrays, or the prediction error array. The method of claim 1, further comprising obtaining a 2-D array of 8×8 block size, JPEG quantized coefficients for moment extraction when the image is a JPEG representation. A method for steganalysis of an image comprising: performing a JPEG decompression of an inputted JPEG representation of the image reconstructing a spatial representation of the image from the JPEG decompression performing entropy decoding of the JPEG representation, to obtain an entropy decoded JPEG representation re-ordering the entropy decoded JPEG representation, to obtain a re-ordered entropy decoded JPEG representation generating multiple non-overlapping, N×N block decompositions of the spatial representation of the image applying block discrete cosine transform (BDCT) to each of the non-overlapping, N×N block decompositions to determine corresponding BDCT coefficient two-dimensional (2-D) arrays forming one or more 8×8 JPEG coefficient arrays from the re-ordered entropy decoded JPEG representation extracting moments using characteristic functions derived based on the spatial representation of the image the BDCT coefficient 2-D arrays, and the one or more 8×8 JPEG coefficient 2-D arrays, to obtain extracted moments generating features based at least on the extracted moments, to obtain generated features classifying the image based at least on the generated features and determining a prediction error array for at least one of the spatial representation of the image, the BDCT coefficient 2-D arrays, or the JPEG coefficient 2-D arrays, wherein determining the prediction error array comprises computing a sign and a magnitude corresponding to a respective element of at least one of the spatial representation of the image, the BDCT coefficient 2-D arrays, or JPEG coefficient 2-D arrays, wherein computing a magnitude comprises computing an expression |a|+|b|−|c|, wherein a, b, and c correspond to elements neighboring the respective element obtaining the sign of the at least one element and computing the product of the sign and the expression to obtain a prediction corresponding to the respective element. Embodiments of the invention also (1) utilize moments of 1-D and 2-D characteristic functions (2) Prediction-error and (3) all wavelet subbands including the low-low subbands. In addition, the method is also effective as a universal steganalyzer for both texture and smooth/non-texture images. When the block size is 8×8, the JPEG coefficients (JPEG quantized 8×8 BDCT coefficients) derived directly from the JPEG image are used to extract features. When JPEG format is provided, the image is first decompressed to spatial representation, from which the features are extracted when the block size is at least one 2×2, 4×4, and 16×16. Image data that is to undergo steganalysis can be provided to embodiments of the invention in at least one of spatial (pixel) and JPEG format. ![]() ![]() The methods combine features extracted from the image spatial representation and from a block discrete cosine transform (BDCT) representation with multiple different block sizes (i.e., N×N) to improve detection of data hidden in texture images. Method and apparatus for steganalysis for texture imagesĮmbodiments of the invention are directed toward methods for steganalysis that improve the detection of steganography in texture images. ![]()
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