Echo hiding is a steganographic technique with audio as carrier. Currently， the steganalysis methods for echo hiding mainly use the cepstral coefficients as handcrafted-features to realize classification. However， when the echo amplitude is low， the detection performance of these traditional methods is not high. Aiming at the low echo amplitude condition， a steganalysis method for echo hiding based on Convolutional Neural Network （CNN） was proposed. Firstly， Short-Time Fourier Transform （STFT） was used to extract the amplitude spectrum coefficient matrix as the shallow feature. Secondly， the deep feature was extracted by the designed CNN framework from the shallow feature. The network framework consisted of four convolutional blocks and three fully connected layers. Finally， the classification results were output by Softmax. The proposed method was steganographically evaluated on three classic echo hiding algorithms. Experimental results indicate that the detection rates of the proposed method under low echo amplitude are 98.62%， 98.53% and 93.20% respectively. Compared with the existing traditional handcrafted-features based methods and deep learning based methods， the proposed method has the detection performance improved by more than 10%.

Moving shadows cause serious problem while segmenting and extracting foreground from video sequences, due to the misclassification of moving shadow as foreground. In order to detect the moving objects accurately, a method based on the similarity between little textured patches was proposed to remove the moving shadows. Firstly, the potential shadows were detected by analyzing the intensity and color properties. Secondly, the shadow detection approach was improved by evaluating the textural similarity between the current frame and the corresponding background model. Finally, the geometric heuristics were imposed to further improve the performance. Experimental results on both indoor and outdoor scenes exhibit that the proposed method succeeds in removing shadows robustly and achieves real-time performance.