Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF

Deep neural networks (DNNs) have been proven extremely susceptible to adversarial examples, which raises special safety-critical concerns for DNN-based autonomous driving stacks (i.e., 3D object detection). Although there are extensive works on image-level attacks, most are restricted to 2D pixel spaces, and such attacks are not always physically realistic in our 3D world. Here we present Adv3D, the first exploration of modeling adversarial examples as Neural Radiance Fields (NeRFs). Advances in NeRF provide photorealistic appearances and 3D accurate generation, yielding a more realistic and realizable adversarial example. We train our adversarial NeRF by minimizing the surrounding objects' confidence predicted by 3D detectors on the training set. Then we evaluate Adv3D on the unseen validation set and show that it can cause a large performance reduction when rendering NeRF in any sampled pose. To generate physically realizable adversarial examples, we propose primitive-aware sampling and semantic-guided regularization that enable 3D patch attacks with camouflage adversarial texture. Experimental results demonstrate that the trained adversarial NeRF generalizes well to different poses, scenes, and 3D detectors. Finally, we provide a defense method to our attacks that involves adversarial training through data augmentation. Project page: https://len-li.github.io/adv3d-we

Coordinated scheduling of intercell production and intercell transportation in the equipment manufacturing industry

<p>Intercell moves are caused by exceptional parts which need to be processed in multiple cells. Intercell cooperation disrupts the cellular manufacturing philosophy of creating independent cells, but is essential to lower the costs for enterprises. This article addresses an intercell scheduling problem considering limited transportation capability. To solve this problem, a two-stage ant colony optimization approach is proposed, in which pre-scheduling and re-scheduling are performed sequentially. To evaluate and optimize the interaction of production and transportation, a transportation benefit function is presented, according to which the scheduling solutions are adjusted. The computational results show that the transportation benefit function is more effective than other strategies, and the proposed approach has significant advantages over CPLEX in both the production dimension and the transportation dimension.</p

Enhancement of Imaging Quality of Interferenceless Coded Aperture Correlation Holography Based on Physics-Informed Deep Learning

Interferenceless coded aperture correlation holography (I-COACH) was recently introduced for recording incoherent holograms without two-wave interference. In I-COACH, the light radiated from an object is modulated by a pseudo-randomly-coded phase mask and recorded as a hologram by a digital camera without interfering with any other beams. The image reconstruction is conducted by correlating the object hologram with the point spread hologram. However, the image reconstructed by the conventional correlation algorithm suffers from serious background noise, which leads to poor imaging quality. In this work, via an effective combination of the speckle correlation and neural network, we propose a high-quality reconstruction strategy based on physics-informed deep learning. Specifically, this method takes the autocorrelation of the speckle image as the input of the network, and switches from establishing a direct mapping between the object and the image into a mapping between the autocorrelations of the two. This method improves the interpretability of neural networks through prior physics knowledge, thereby remedying the data dependence and computational cost. In addition, once a final model is obtained, the image reconstruction can be completed by one camera exposure. Experimental results demonstrate that the background noise can be effectively suppressed, and the resolution of the reconstructed images can be enhanced by three times

Flexible Image Reconstruction in the Orbital Angular Momentum Holography with Binarized Airy Lens

The orbital angular momentum (OAM) holography has been marked a path to achieving ultrahigh capacity holographic information systems. However, the practical applicability of the OAM holography is limited by the complicated optical setup and unadjustable image intensity and position. Here, a decoding method is proposed by using a binarized phase map derived from an autofocusing Airy beam. By adjusting the parameters of the phase map, the position and intensity distribution of the reconstructed image become flexibly adjustable. In addition, the cross-talk between different image channels can be effectively reduced thanks to the abruptly autofocusing capability of the Airy beams. As a result, the quality and practicability of the OAM holography can be greatly enhanced