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1. Decentralized Attribution of Generative Models

   Kim, C; Ren, Y; Yang, Y. (September 2020, ICLR 2021)

   null (Ed.)

   Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all existing user-end models. However, this approach is not scalable in a reality where the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes. 

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2. Attributable Watermarking of Speech Generative Models

   https://doi.org/10.1109/ICASSP43922.2022.9746578  

   Cho, Yongbaek; Kim, Changhoon; Yang, Yezhou; Ren, Yi (January 2022, Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing)

   Generative models are now capable of synthesizing images, speeches, and videos that are hardly distinguishable from authentic contents. Such capabilities cause concerns such as malicious impersonation and IP theft. This paper investigates a solution for model attribution, i.e., the classification of synthetic contents by their source models via watermarks embedded in the contents. Building on past success of model attribution in the image domain, we discuss algorithmic improvements for generating user-end speech models that empirically achieve high attribution accuracy, while maintaining high generation quality. We show the tradeoff between attributability and generation quality under a variety of attacks on generated speech signals attempting to remove the watermarks, and the feasibility of learning robust watermarks against these attacks. 

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3. Adversarial training and attribution methods enable evaluation of robustness and interpretability of deep learning models for image classification

   https://doi.org/10.1103/PhysRevE.110.054310  

   Santos, Flávio_A O; Zanchettin, Cleber; Lei, Weihua; Nunes_Amaral, Luís A (November 2024, Physical Review E)

   Deep learning models have achieved high performance in a wide range of applications. Recently, however, there have been increasing concerns about the fragility of many of those models to adversarial approaches and out-of-distribution inputs. A way to investigate and potentially address model fragility is to develop the ability to provide interpretability to model predictions. To this end, input attribution approaches such as Grad-CAM and integrated gradients have been introduced to address model interpretability. Here, we combine adversarial and input attribution approaches in order to achieve two goals. The first is to investigate the impact of adversarial approaches on input attribution. The second is to benchmark competing input attribution approaches. In the context of the image classification task, we find that models trained with adversarial approaches yield dramatically different input attribution matrices from those obtained using standard techniques for all considered input attribution approaches. Additionally, by evaluating the signal-(typical input attribution of the foreground)-to-noise (typical input attribution of the background) ratio and correlating it to model confidence, we are able to identify the most reliable input attribution approaches and demonstrate that adversarial training does increase prediction robustness. Our approach can be easily extended to contexts other than the image classification task and enables users to increase their confidence in the reliability of deep learning models. 

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4. Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

   https://doi.org/3412815.3416893  

   Tan, Sarah; Soloviev, Matvey; Hooker, Giles; Wells, Martin (October 2020, Foundations of data science)

   null (Ed.)

   Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative points for each class -- prototypes. We introduce a new distance for Gradient Boosted Tree models, and propose new, adaptive prototype selection methods with theoretical guarantees, with the flexibility to choose a different number of prototypes in each class. We demonstrate our methods on random forests and gradient boosted trees, showing that the prototypes can perform as well as or even better than the original tree ensemble when used as a nearest-prototype classifier. In a user study, humans were better at predicting the output of a tree ensemble classifier when using prototypes than when using Shapley values, a popular feature attribution method. Hence, prototypes present a viable alternative to feature-based explanations for tree ensembles. 

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5. BANANA: when Behavior ANAlysis meets social Network Alignment

   https://doi.org/10.24963/ijcai.2020/200  

   Ren, Fuxin; Zhang, Zhongbao; Zhang, Jiawei; Su, Sen; Sun, Li; Zhu, Guozhen; Guo, Congying (July 2020, International Joint Conference on Artificial Intelligence)

   null (Ed.)

   Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. 

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