With the widespread application of smart contracts and the expansion of asset management scale, various new attacks continue to emerge, and a method that can adapt to new vulnerabilities more quickly is urgently needed. Although deep learning methods have shown superior performance in vulnerability detection, their dependence on a large number of labeled samples limits their applicability in new vulnerability scenarios. Therefore, this article introduces a dual adversarial domain adaptation (DADA) approach. This approach consists of two generators and two discriminators. First, the source generator is pretrained with extensive labeled known vulnerability samples in the source domain to extract discriminative features, and its parameters are shared with the target generator. Subsequently, the features of the source and target domain samples are input into the source discriminator, and the target generator is guided to learn domain-invariant features through adversarial training; at the same time, the target domain discriminator is introduced to further weaken its dependence on the distribution of source domain features, thereby improving its adaptability to new vulnerabilities. We conducted experimental evaluations on public datasets, and the results show that our proposed method outperforms six mainstream deep learning-based detection methods. We applied domain adaptation methods to smart contract vulnerability detection for the first time, providing a reference for small sample learning in this field in the future.

Pyramidal Lucas-Kanade (LK) optical flow is a real-time registration technique widely employed by a variety of cutting edge consumer applications. Traditionally, the LK algorithm is applied selectively to image feature points that have strong spatial variation, which include outliers in textured areas. To detect and discard the falsely selected features, previous methods generally assess the goodness of each feature after the flow computation is completed. Such a screening process incurs additional cost. This paper provides a handy (but not obvious) tool for the users of the LK algorithm to remove false features without degrading the algorithm's efficiency. We propose a confidence predictor, which evaluates the ill-posedness of an LK system directly from the underlying data, at a cost lower than solving the system. We then incorporate our confidence predictor into the course-to-fine LK registration to dynamically detect false features and terminate their flow computation at an early stage. This improves the registration accuracy by preventing the error propagation and maintains (or increases) the computation speed by saving the runtime on false features. Experimental results on state-of-the-art benchmarks validate that our method is more accurate and efficient than related works.

Intensity degradations are a familiar problem for fluorescein angiogram sequences. In this paper, we attempt to restore a noisy fluorescein angiogram, and to keep the high intensity pixels from degrading. To this end, we incorporate a new constraint, called intensity constraint, to Miller's regularization formulation with a smoothness constraint. Considering the specified requirement for fluorescein angiograms, we also modify the Q-th order converging algorithm for implementation purpose. In our scheme, including its formulation and implementation, image restoration can not only handle the traditional problems, such as blur and noise, but also achieve an important feature, intensity preservation. The experiments show that our approach has satisfactory results in the two aspects.

In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient nonlinear sharpening method, which can be applied for edge detection. By employing four directional structuring elements, ASMG filtering has the capability of selecting the directional structuring element with the maximum response, whose direction varies depending on the change of directional edges. In addition, by comparing the variance of the moving structuring window with the base variance, the ASMG filter provides an adaptive algorithm for morphological operations. The experimental results show that the ASMG filter is better than the traditional operators in edge detection and noise suppression.