Existing deep learning-based malicious code detection methods have problems such as weak deep-level feature extraction capability, relatively complex model and insufficient model generalization capability. At the same time, code reuse phenomenon occurred in large number of malicious samples of the same type, resulting in similar visual features of the code. This similarity can be used for malicious code detection. Therefore, a malicious code detection method based on multi-channel image visual features and AlexNet was proposed. In the method, the codes to be detected were converted into multi-channel images at first. After that, AlexNet was used to extract and classify the color texture features of the images, so as to detect the possible malicious codes. Meanwhile, the multi-channel image feature extraction, the Local Response Normalization(LRN) and other technologies were used comprehensively, which effectively improved the generalization ability of the model with effective reduction of the complexity of the model. The Malimg dataset after equalization was used for testing, the results showed that the average classification accuracy of the proposed method was 97.8%, and the method had the accuracy increased by 1.8% and the detection efficiency increased by 60.2% compared with the VGGNet method. Experimental results show that the color texture features of multi-channel images can better reflect the type information of malicious codes, the simple network structure of AlexNet can effectively improve the detection efficiency, and the local response normalization can improve the generalization ability and detection effect of the model.

The proposed JPEG 2000 real-time quantization watermarking algorithm was used in an improved online bank pension distribution system. The system was based on fingerprint recognition and digital watermarking technologies. In the client side, real-time quantization watermark was embedded into the sampled fingerprint image in the JPEG 2000 coding pipeline; then the compressed bit-stream was sent to the server side. In the server side, the watermark was extracted from the compressed bit-stream in the JPEG 2000 decoding pipeline; then the decompressed fingerprint image and extracted watermark were used to verify users identification. Experiments showed when typical fingerprint image was compressed to 1/4~1/20 of its original size, the embedded watermark could be exactly extracted, and fingerprint recognition rate remained almost the same after lossy compression. The system has a better interaction performance in the band-limited network situation, and is very promising in the E-business applications.