The invention provides an image forensics method for a natural image and a compressed and tampered image based on DWT. According to the method, the natural image and the compressed image based on the DWT can be effectively distinguished, meanwhile, good distinguishability is achieved on certain specific image tampering carrying out compression trace elimination on the compressed image, the joint probability histogram of a wavelet transform coefficient of the natural image and the tampered image is calculated through the method, the histogram is normalized, then Hough transform is carried out, the mean value, variance value, skewness value and kurtosis value of a Hough transform coefficient matrix are extracted as characteristic values of a support vector machine, and a training set is formed by the characteristic values. A classification model is generated by the support vector machine through the training set in a training mode, unknown characteristic value samples are classified through the model, and whether compression or anti-compression forensics processing is carried out on an image or not is judged. The method is stable in performance, easy and convenient to implement, efficient, high in accuracy and suitable for forensics detection of the natural image and the tampered image in other aspects.

Image forensics method for natural image and compressed and tampered image based on DWT

The invention discloses an image saliency based method for detecting an automatic landing site of a UAV (unmanned aerial vehicle). The method comprises the following steps: (1), saliency detection of the UAV: salient region detection on acquired UAV flight image data I (I, j) from the angle of frequency domain transformation through discrete cosine transform, that is, an UAV flight sequence diagram I is input and a UAV salient image Inum_SIG is output; and (2), acquisition of the UAV image position: the UAV is positioned in the salient image, that is, the UAV salient image Inum_SIG and threshold delta are input, and UAV image sequence coordinates (xnum_out, ynum_out) are output. The image saliency based method for detecting the automatic landing site of the UAV has the advantages that the UAV can be detected and positioned rapidly, the accuracy and the reliability are high and the like.

Image saliency based method for detecting automatic landing site of UAV (unmanned aerial vehicle)

The invention discloses a method for identifying a natural image and a computer generated image based on DCT (Discrete Cosine Transformation)-domain statistic characteristics. The method is characterized by comprising the following steps of (1) firstly, carrying out gaussian fuzzy processing and dimension reduction processing on an image to be detected, carrying out 8*8 no-repeat DCT on3 channels R, G and B of the image to obtain a 8*8 partitioning DCT coefficient matrix; (2) carrying out statistics on the distribution of a first significant figure of the DCT-domain AC coefficient of each channel to obtain three probability distribution curves; and (3) calculating an average absolute differential of the three probability distribution curves, if the average absolute differential is greater than a set threshold T to prove that the overlapping degree of the three curves is not sufficient, judging the image to be detected as the natural image, otherwise, judging the image to be detected as the computer generated image. Experimental results show that due to the algorithm, the identification accuracy rate of the natural image and the computer generated image is increased. Compared with the existing algorithm, the method has the advantages that the identification rate is higher, the calculating amount is small, the implementation is easy, and the identification accuracy rate reaches 95.22%.

Zhuang Donggang

Papers

Method for identifying natural image and computer generated image based on DCT (Discrete Cosine Transformation)-domain statistic characteristics