Although Convolutional Neural Networks (CNNs) have achieved remarkable success in image classification, most CNNs use image datasets in the Red-Green-Blue (RGB) color space (one of the most commonly used color spaces). The existing literature regarding the influence of color space use on the performance of CNNs is limited. This paper explores the impact of different color spaces on image classification using CNNs. We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets, each converted to nine color spaces. We find that color space selection can significantly affect classification accuracy, and that some classes are more sensitive to color space changes than others. Different color spaces may have different expression abilities for different image features, such as brightness, saturation, hue, etc. To leverage the complementary information from different color spaces, we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture. Our experiments show that our proposed model can outperform the single-color-space models on most datasets. We also find that our method is simple, flexible, and compatible with any CNN and image dataset.

The growing popularity and application of Web services have led to increased attention regarding the vulnerability of software based on these services. Vulnerability testing examines the trustworthiness and reduces the security risks of software systems. This paper proposes a worst-input mutation approach for testing Web service vulnerability based on Simple Object Access Protocol (SOAP) messages. Based on characteristics of SOAP messages, the proposed approach uses the farthest neighbor concept to guide generation of the test suite. The corresponding automatic test case generation algorithm, namely, the Test Case generation based on the Farthest Neighbor (TCFN), is also presented. The method involves partitioning the input domain into sub-domains according to the number and type of SOAP message parameters in the TCFN, selecting the candidate test case whose distance is the farthest from all executed test cases, and applying it to test the Web service. We also implement and describe a prototype Web service vulnerability testing tool. The tool was applied to the testing of Web services on the Internet. The experimental results show that the proposed approach can find more vulnerability faults than other related approaches.