With the rapid development of technology such as social networks, e-commerce, and mobile internet, a large amount of text data with emotional content is generated. Researchers and practitioners have been actively engaged in the development of sentiment analysis (SA) systems to analyze this content. However, SA systems are not robust enough. To address this issue, we propose MTSA, a multi-granularity metamorphic testing framework that automatically generates test cases to evaluate the SA system's robustness. We summarize fourteen metamorphic relations (MRs) at different granularities, including character level, word level, and sentence level. MTSA employs these MRs to generate test cases that simulate real user input to evaluate SA systems. In our experiment, we employed MTSA to evaluate nine different applications, including four commercial SA systems, four academic models, and one software package. The results show that MTSA achieves error triggering rates (ETR) in the range of 3.36% to 73.03 % across the nine software under test (SUT). Our empirical evaluation demonstrates that all types of SA systems, including business SA systems, academic models, and software packages, have an average ETR above 25%. Therefore, MTSA is capable of generating valid test cases to evaluate SA systems.

With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.