Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis

Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image synthesis for a specific subject, e.g., a human face or an artistic style, by fine-tuning the generic text-to-image model with a few images from that subject. Nevertheless, misuse of subject-driven image synthesis may violate the authority of subject owners. For example, malicious users may use subject-driven synthesis to mimic specific artistic styles or to create fake facial images without authorization. To protect subject owners against such misuse, recent attempts have commonly relied on adversarial examples to indiscriminately disrupt subject-driven image synthesis. However, this essentially prevents any benign use of subject-driven synthesis based on protected images. In this paper, we take a different angle and aim at protection without sacrificing the utility of protected images for general synthesis purposes. Specifically, we propose GenWatermark, a novel watermark system based on jointly learning a watermark generator and a detector. In particular, to help the watermark survive the subject-driven synthesis, we incorporate the synthesis process in learning GenWatermark by fine-tuning the detector with synthesized images for a specific subject. This operation is shown to largely improve the watermark detection accuracy and also ensure the uniqueness of the watermark for each individual subject. Extensive experiments validate the effectiveness of GenWatermark, especially in practical scenarios with unknown models and text prompts (74% Acc.), as well as partial data watermarking (80% Acc. for 1/4 watermarking). We also demonstrate the robustness of GenWatermark to two potential countermeasures that substantially degrade the synthesis quality

Generated Graph Detection

Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. Hence it is essential to regulate the prevalence of generated graphs. To tackle this problem, we pioneer the formulation of the generated graph detection problem to distinguish generated graphs from real ones. We propose the first framework to systematically investigate a set of sophisticated models and their performance in four classification scenarios. Each scenario switches between seen and unseen datasets/generators during testing to get closer to real-world settings and progressively challenge the classifiers. Extensive experiments evidence that all the models are qualified for generated graph detection, with specific models having advantages in specific scenarios. Resulting from the validated generality and oblivion of the classifiers to unseen datasets/generators, we draw a safe conclusion that our solution can sustain for a decent while to curb generated graph misuses.Comment: Accepted by ICML 202

Vision-language models boost food composition compilation

Nutrition information plays a pillar role in clinical dietary practice, precision nutrition, and food industry. Currently, food composition compilation serves as a standard paradigm to estimate food nutrition information according to food ingredient information. However, within this paradigm, conventional approaches are laborious and highly dependent on the experience of data managers, they cannot keep pace with the dynamic consumer market and resulting in lagging and missing nutrition data and earlier machine learning methods unable to fully understand food ingredient statement information or ignored the characteristic of food image. To this end, we developed a novel vision-language AI model, UMDFood-VL, using front-of-package labeling and product images to accurately estimate food composition profiles. In order to drive such large model training, we established UMDFood-90k, the most comprehensive multimodal food database to date. The UMDFood-VL model significantly outperformed convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on a variety of nutrition value estimations. For instance, we achieved macro-AUCROC up to 0.921 for fat value estimation, which satisfied the practice requirement of food composition compilation. This performance shed the light to generalize to other food and nutrition-related data compilation and catalyzed the evolution of other food applications.Comment: 31 pages, 5 figure

Structure of Titan ’ s induced magnetosphere under varying background magnetic fi eld conditions: Survey of Cassini magnetometer data from fl ybys TA – T85

Cassini magnetic field observations between 2004 and 2012 suggest the ambient field conditions near Titan’s orbit to differ significantly from the frequently applied pre-Cassini picture (background magnetic field homogeneous and perpendicular to Titan’s orbital plane, stationary upstream conditions). In this study, we analyze the impact of these varying background field conditions on the structure of Titan’s induced magnetosphere by conducting a systematic survey of Cassini magnetic field observations in the interaction region during flybys TA–T85 (July 2004–July 2012). We introduce a set of criteria that allow to identify deviations in the structure of Titan’s induced magnetosphere—as seen by the Cassini magnetometer (MAG)—from the picture of steady-state field line draping. These disruptions are classified as “weak”, “moderate”, or “strong”. After applying this classification scheme to all available Titan encounters, we survey the data for a possible correlation between the disruptions of the draping pattern and the ambient magnetospheric field conditions, as characterized by Simon et al. [2010a]. Our major findings are: (1) When Cassini is embedded in the northern or southern lobe of Saturn’s magnetodisk within a ` 3 h interval around closest approach, Titan’s induced magnetosphere shows little or no deviations at all from the steady-state draping picture. (2) Even when Titan is embedded in perturbed current sheet fields during an encounter, the notion of draping the average background field around the moon’s ionosphere is still applicable to explain MAG observations from numerous Titan flybys. (3) Only when Titan is exposed to intense north- south oscillations of Saturn’s current sheet at the time of an encounter, the signatures of the moon’s induced magnetosphere may be completely obscured by the ambient field perturbations. (4) So far, T70 is the only flyby that fully meets the idealized pre-Cassini picture of the Titan interaction (steady background field perpendicular to Titan’s orbital plane, steady upstream flow, unperturbed induced magnetosphere).Fil: Simon, Sven. University of Cologne. Institute of Geophysics and Meteorology; AlemaniaFil: van Treeck, Shari C.. University of Cologne. Institute of Geophysics and Meteorology; AlemaniaFil: Wennmacher, Alexandre. University of Cologne. Institute of Geophysics and Meteorology; AlemaniaFil: Saur, Joachim. University of Cologne. Institute of Geophysics and Meteorology; AlemaniaFil: Neubauer, Fritz M.. University of Cologne. Institute of Geophysics and Meteorology; AlemaniaFil: Bertucci, Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio(i); ArgentinaFil: Dougherty, Michele K.. Imperial College Of Science And Technology. Space and Atmospheric Physics Group; Reino Unid

Model-based autonomous system for performing dexterous, human-level manipulation tasks

This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dual-arm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.United States. Defense Advanced Research Projects Agency. Autonomous Robotic Manipulation Progra

Time‐dependent global MHD simulations of Cassini T32 flyby: From magnetosphere to magnetosheath

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95383/1/jgra19655.pd

3D global multi‐species Hall‐MHD simulation of the Cassini T9 flyby

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94961/1/grl23831.pd

Comparisons between MHD model calculations and observations of Cassini flybys of Titan

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94689/1/jgra18227.pd