The use of Stable Diffusion models to generate realistic images has become a popular topic in recent years. However, this technology has also raised concerns about the potential harm it may cause to the copyright holders, particularly in the realm of art where these synthesized images can closely resemble the original work. As these synthesized images are hard for humans to distinguish from authentic ones, it is of great importance to develop methods that may identify them. In this paper, we propose a deep learning-based approach to detect synthesized images using information in the frequency domain. Since there exists no well-established dataset of images synthesized by stable diffusion models, in order to train and evaluate our network we generated a representative dataset consisting of carefully selecting human-created authentic images and synthesized animation images generated by the Stable Diffusion models. We chose to use Disney-style animated content for our case study, given its significance in the realm of intellectual property protection. Experimental results demonstrated that our proposed model outperforms humans and other state-of-the-art methods, achieving an accuracy rate of 99.46%.

Numerous static taint analysis techniques have recently been proposed for identifying information flows in mobile applications. These techniques are often optimized and evaluated on a set of synthetic benchmarks, which makes the comparison results difficult to generalize. Moreover, the techniques are commonly compared under different configuration setups, rendering the comparisons inaccurate. In this paper, we provide a large, controlled, and independent comparison of the three most prominent static taint analysis tools:FlowDroid,Amandroid, andDroidSafe. We align the configuration setup for the tools and evaluate them on both a set of common benchmarks and on real applications from the Google Play app store. We further evaluate the effectiveness of additional reflection handling mechanism implemented byDroidRA, applying it to each of the evaluated tools. We compare the results of our analysis to the results reported in previous studies, identify main reasons for inaccuracy in existing tools, and provide suggestions for future research.