Revisiting Data-Free Knowledge Distillation with Poisoned Teachers

Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model. However, the security of the synthetic or out-of-distribution (OOD) data required in data-free KD is largely unknown and under-explored. In this work, we make the first effort to uncover the security risk of data-free KD w.r.t. untrusted pre-trained models. We then propose Anti-Backdoor Data-Free KD (ABD), the first plug-in defensive method for data-free KD methods to mitigate the chance of potential backdoors being transferred. We empirically evaluate the effectiveness of our proposed ABD in diminishing transferred backdoor knowledge while maintaining compatible downstream performances as the vanilla KD. We envision this work as a milestone for alarming and mitigating the potential backdoors in data-free KD. Codes are released at https://github.com/illidanlab/ABD.Comment: Accepted to ICML 202

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks

Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, a recent work has introduced a watermarking algorithm and utilized the null-hypothesis test as a post-hoc ownership verification on the imitation models. However, we find that it is possible to detect those watermarks via sufficient statistics of the frequencies of candidate watermarking words. To address this drawback, in this paper, we propose a novel Conditional wATERmarking framework (CATER) for protecting the IP of text generation APIs. An optimization method is proposed to decide the watermarking rules that can minimize the distortion of overall word distributions while maximizing the change of conditional word selections. Theoretically, we prove that it is infeasible for even the savviest attacker (they know how CATER works) to reveal the used watermarks from a large pool of potential word pairs based on statistical inspection. Empirically, we observe that high-order conditions lead to an exponential growth of suspicious (unused) watermarks, making our crafted watermarks more stealthy. In addition, \cater can effectively identify the IP infringement under architectural mismatch and cross-domain imitation attacks, with negligible impairments on the generation quality of victim APIs. We envision our work as a milestone for stealthily protecting the IP of text generation APIs.Comment: accepted to NeurIPS 202

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus

One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image. Our project webpage: https://sudo-ai-3d.github.io/One2345plus_page

Sun and Salt - A Temporary Off-Grid Device for Families in Developing Countries

As we’re living in a modern society, most of us have been connected to the water and electricity grid, However, there’re still billions of people who are still lack of reliable water and electricity resource. In some developing countries like India and Philippines, the demand for electricity and water are far more than actual production, which causes frequent electricity and water shortage problems. And more importantly, water shortage and electricity shortage, in many cases, don’t come alone. Sun and salt, as abundant resources for us, could bring us clean water and electricity in an off-grid way. The principle is so simple that we could easily try them out at home: Using the heat of the sun to distill water, and to use salt water to produce electricity. The “sun and salt” off-grid device help people to make preparations for frequent short-term electricity and water shortage. It has two parts: the outer part is a water distiller, which could be used to clarify gray water for daily cleaning or drinking, the inner part is adjustable light using salt water as the battery. A USB socket is also available on this device, which could be used to charge your phone. During the daytime, it could be used as a power bank and water distiller, and during night time it could be used to bright up your room

Turning a Curse Into a Blessing: Enabling Clean-Data-Free Defenses by Model Inversion

It is becoming increasingly common to utilize pre-trained models provided by third parties due to their convenience. At the same time, however, these models may be vulnerable to both poisoning and evasion attacks. We introduce an algorithmic framework that can mitigate potential security vulnerabilities in a pre-trained model when clean data from its training distribution is unavailable to the defender. The framework reverse-engineers samples from a given pre-trained model. The resulting synthetic samples can then be used as a substitute for clean data to perform various defenses. We consider two important attack scenarios -- backdoor attacks and evasion attacks -- to showcase the utility of synthesized samples. For both attacks, we show that when supplied with our synthetic data, the state-of-the-art defenses perform comparably or sometimes even better than the case when it's supplied with the same amount of clean data.Comment: Because of an equation and author informational error, this paper has been withdrawn by the submitte

Effects and Mechanisms of Exosomes from Different Sources in Cerebral Ischemia

Cerebral ischemia refers to the symptom of insufficient blood supply to the brain. Cells of many different origins participate in the process of repairing damage after cerebral ischemia occurs, in which exosomes secreted by the cells play important roles. For their characteristics, such as small molecular weight, low immunogenicity, and the easy penetration of the blood–brain barrier (BBB), exosomes can mediate cell-to-cell communication under pathophysiological conditions. In cerebral ischemia, exosomes can reduce neuronal damage and improve the brain microenvironment by regulating inflammation, mediating pyroptosis, promoting axonal growth, and stimulating vascular remodeling. Therefore, exosomes have an excellent application prospect for the treatment of cerebral ischemia. This article reviews the roles and mechanisms of exosomes from different sources in cerebral ischemia and provides new ideas for the prevention and treatment of cerebral ischemia

Laser Welding of BTi-6431S High Temperature Titanium Alloy

A new type of high temperature titanium alloy, BTi-6431S, has recently become the focus of attention as a potential material for aircraft engine applications, which could be used up to 700 °C. Pulsed laser welding was used to butt join the BTi-6431S titanium alloy in order to understand the feasibility of using fusion-based welding techniques on this material. The effect of laser energy on the microstructure and mechanical properties of the joints was investigated. The microstructural features of the joints were characterized by means of microscopy and X-ray diffraction. Tensile testing was conducted at both room temperature and high temperature to simulate potential service conditions. The results show that the microstructure of the laser welded joints consists of primary α phase and needle α’ phase, while the microstructure of the heat affected zone consists of α, β, and needle α’ phases. The tensile strength of the welded joints at room temperature was similar to that of the base material, despite a reduction in the maximum elongation was observed. This was related to the unfavorable microstructure in the welded joints. Nonetheless, based on these results, it is suggested that laser welding is a promising joining technique for the new BTi-6431S titanium alloy for aerospace applications