3D point cloud has been wildly used in security crucial domains, such as self-driving and 3D face recognition. Backdoor attack is a serious threat that usually destroy Deep Neural Networks (DNN) in the training stage. Though a few 3D backdoor attacks are designed to achieve guaranteed attack efficiency, their deformation will alarm human inspection. To obtain invisible backdoored point cloud, this paper proposes a novel 3D backdoor attack, named IBAPC, which generates backdoor trigger in the graph spectral domain. The effectiveness is grounded by the advantage of graph spectral signal that it can induce both global structure and local points to be responsible for the caused deformation in spatial domain. In detail, a new backdoor implanting function is proposed whose aim is to transform point cloud to graph spectral signal for conducting backdoor trigger. Then, we design a backdoor training procedure which updates the parameter of backdoor implanting function and victim 3D DNN alternately. Finally, the backdoored 3D DNN and its associated backdoor implanting function is obtained by finishing the backdoor training procedure. Experiment results suggest that IBAPC achieves SOTA attack stealthiness from three aspects including objective distance measurement, subjective human evaluation, graph spectral signal residual. At the same time, it obtains competitive attack efficiency. The code is available at https://github.com/f-lk/IBAPC.

3D mesh classification deep neural network (3D DNN) has been widely applied in many safety-critical domains. Backdoor attack is a serious threat that occurs during the training stage. Previous backdoor attacks from 2D image and 3D point cloud domains are not suitable for 3D mesh due to data structure restrictions. Therefore, in a pioneering effort, this paper presents two types of backdoor attacks on 3D mesh. Specifically, the first attack is a Mesh Geometrical Feature guided 3D Mesh Backdoor Attack named MGF-MBA. Most 3D DNNs have to convert 3D mesh to a regular matrix (mesh geometrical feature), which is a refinement of the input 3D mesh. The 3D DNN directly learns the 3D shape from the mesh geometrical feature, which enables attackers to implant backdoor through it. Hence, the proposed MGF-MBA generates a backdoored 3D mesh under the guidance of mesh geometrical feature. The second attack is a Remeshing based 3D Mesh Backdoor Attack named ReMBA. The quality of samples backdoored by exiting backdoor attacks always decrease. Although many efforts have been made to reduce the descent in quality in return for stealthiness, the descent persists. For better stealthiness, we regard the backdoor implantation process as a way to increase the quality of backdoored sample rather than a way to reduce it. Specifically, ReMBA designs a new isotropic remeshing method that attempts to represent a 3D mesh by equilateral triangles while keeping the number of vertices, edges and faces unchanged. Numerous experimental results show that both MGF-MBA and ReMBA achieve guaranteed attack performance on 3D DNNs. Furthermore, transferability experiments demonstrate that ReMBA can even attack 3D point cloud networks with an increased ability to resist defenses.

Hyperspectral endmember extraction (HEE) is an essential part of remote-sensing image processing. There have been recent attempts to model the HEE as a multiobjective optimization problem and apply multiobjective evolutionary algorithms to solve the problem. However, because of the large HEE search space, it is difficult for the current algorithms to achieve exploration–exploitation balance, and they easily stall prematurely. To address these issues, this article proposes a multistrategy evolutionary multiobjective method based on roulette wheel selection and the genetic algorithm (RWS-GA) for endmember extraction. This method designs two parallel algorithms corresponding to global exploration and local exploitation. In the RWS-GA, an improved NSGA-II method, adopting a novel method to sort individuals on the same front instead of the crowding distance, is proposed to divide individuals into superior and inferior subpopulations. Thereafter, different modified population update strategies are utilized for subpopulations based on characteristics. Pixels that appear more frequently in the population are considered to perform better to have a higher probability of forming an endmember set with other pixels. In addition, excellent individuals often exhibit a higher probability of including endmembers compared with inferior individuals. Considering the abovementioned opinions, roulette wheel selection is performed on the inferior subpopulation for global search. Meanwhile, the superior subpopulation is responsible for local search based on the genetic algorithm (GA). Furthermore, an offspring complement mechanism (OCM) is presented to prevent duplicate individuals from appearing in historical archives. Numerous comparative experiments show that the proposed method is superior to other endmember extraction methods in three real-world datasets.