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DeSI: Deepfake Source Identifier for Social Media

DeSI: Deepfake Source Identifier for Social Media

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Narayan, Kartik Agarwal, Harsh Mittal, Surbhi Thakral, Kartik Kundu, Suman Vatsa, Mayank Singh, Richa IIT Jodhpur Karwar Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.

关键词： Deepfakes computer vision Social networking (online) Scalability conferences Blogs Sociology

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Mitigating Paucity of Data in Sinusoid Characterization Using Generative Synthetic Noise

Mitigating Paucity of Data in Sinusoid Characterization Usin...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sattarzadeh, Sam Shalmani, Shervin Manzuri Azad, Shervin Goldspot Discoveries Corp Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although the remarkable breakthrough offered by Deep Learning (DL) models in numerous computer vision tasks, the need to acquire large amounts of high-quality natural data and fine-grained annotations is a shortcoming that fundamentally increases the cost and time devoted to training these models in real-world applications. Hence, synthetic datasets are considered reliable alternatives that can reduce the data acquisition by replacing or merging with natural data or effective pre-training of the models. To this end, in this work, we propose a novel approach to integrate structural data structures with the synthetic noise structures learned by unsupervised models that mimic the noise structures in natural data. Based on the proposed approach, we introduce the Sinusoid Feature recognition (SFR) dataset, which contains hard-to-detect fixed-period sinusoid waves. While the previous works in this regard use generative models to sample synthetic data to inflate the training set, we instead apply unsupervised learning models to generate deep synthetic noise which makes training models in the proposed dataset more challenging. We evaluate the segmentation, image reconstruction, and sinusoid characterization models pre-trained or fully trained on the synthetic SFR dataset on a private dataset of grayscale Acoustic Tele-Viewer (ATV) images. Experimental results show that supervision on our proposed synthetic dataset can improve the accuracy of the models by 3-4% via pre-training, and by 17-27% via ad-hoc training while dealing with challenging, realistic real-world images.

关键词： Training Deep learning Image segmentation computer vision Costs Computational modeling Data acquisition

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Efficient Two-stage Model Retraining for Machine Unlearning

Efficient Two-stage Model Retraining for Machine Unlearning

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Junyaup Woo, Simon S. PurpleChips Seoul South Korea Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the rise of the General Data Protection Regulation (GDPR), user data holders should guarantee the "individual's right to be forgotten". It means user data holders must completely remove user data when they receive the request. However, enabling a deep learning model to exclude specific data used during training is challenging. We cannot easily define the meaning of "forgetting" in deep learning and how to achieve it. To address this issue, we propose an efficient machine unlearning architecture to be used for computer vision classification models. Our approach consists of two-stage models, where in the first stage we enables a deep learning model that loses information with contrastive labels in the requested dataset. Second, we retrain the first stage output model with knowledge distillation (KD). Using this two-stage approach, we can substantiate the removal or forgetness of the requested dataset in the deep learning model. With various datasets used for multimedia applications, we demonstrate that our approach achieves performance on par or even higher accuracy than the original model, while effectively removing the requested data.

关键词： Deep learning Training computer vision Computational modeling conferences computer architecture Data models

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Few-Shot Image Classification Along Sparse Graphs

Few-Shot Image Classification Along Sparse Graphs

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Comer, Joseph F. Jacobson, Philip L. Hoffmann, Heiko HRL Labs LLC 3011 Malibu Canyon Rd Malibu CA 90265 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a new perspective for data distributions in the feature space of a deep network and show how to exploit this perspective for few-shot learning. First, we observe that nearest neighbors in the feature space are with high probability members of the same class while generally two random points from one class are not much closer to each other than two points between classes. This observation suggests that classes in feature space form sparse, loosely connected graphs instead of dense clusters. To exploit this property, we propose using label propagation to the nearest unlabeled data and then using a kernel PCA reconstruction error as decision boundary in feature-space for the data distribution of each class. Using this method, which we call "K-Prop," we demonstrate largely improved few-shot learning performances (e.g., 83% accuracy for 1-shot 5-way classification on the RESISC45 satellite-images dataset) for datasets for which a backbone network can be trained to produce high within-class nearest-neighbor probabilities. We demonstrate this relationship using six different datasets.

关键词： Target tracking Limiting Shape Training data Streaming media pattern recognition Reliability

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling conferences Reinforcement learning Predictive models

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Does Federated Dropout actually work?

Does Federated Dropout actually work?

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Gary Charles, Zachary Garrett, Zachary Rush, Keith Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Model sizes are limited in Federated Learning due to network bandwidth and on-device memory constraints. The success of increasing model sizes in other machine learning domains motivates the development of methods for training large-scale models in Federated Learning. To this end, [3] draws inspiration from dropout and proposes Federated Dropout: an algorithm where clients train randomly selected subsets of a larger server model. Despite the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure the performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations that suggest that the best performing variations of Federated Dropout approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, ensembling naturally leverages the parallelizable nature of Federated Learning-recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling's strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought with more sophisticated ensembling strategies e.g., via boosting.

关键词： Training Measurement computer vision Machine learning algorithms conferences Computational modeling Memory management

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True Black-Box Explanation in Facial Analysis

True Black-Box Explanation in Facial Analysis

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mery, Domingo Pontificia Univ Catolica Chile Sch Engn Dept Comp Sci Santiago Chile

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

When explaining a recognition approach that can be used in facial analysis, e.g. face verification, face detection, attribute recognition, etc., the task is to answer: how relevant are the parts of a given image to establish the recognition. In many cases, however, the trained models cannot be manipulated and must be treated as "black-boxes". In this paper, we present a saliency map methodology, called MinPlus, that can be used to explain any facial analysis approach with no manipulation inside of the recognition model, because it only needs the input-output function of the black-box 'fx'. The key idea of the method is based on how the probability of recognition of the given image changes when it is perturbed. Our method removes and aggregates different parts of the image, and measures contributions of these parts individually and in-collaboration as well. We test and compare our method in four different scenarios: face verification (with ArcFace), face expression recognition (with Xception), face detection (with MTCNN) and masked face detection (with YOLOv5s). We conclude that MinPlus achieves saliency maps that are stable and interpretable to humans. In addition, our method shows promising results in comparison with other state-of-the-art methods like AVG, LIME and RISE. This paper presents good insights into any facial analysis approach. It can be used to highlight the most relevant areas that an algorithm takes into account to carry out the recognition process.

关键词： Analytical models Image recognition Face recognition Aggregates conferences Face detection Task analysis

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LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction

LayoutFormer++: Conditional Graphic Layout Generation via Co...

引用

2023 ieee/CVF conference on computer vision and pattern recognition, cvpr 2023

作者： Jiang, Zhaoyun Guo, Jiaqi Sun, Shizhao Deng, Huayu Wu, Zhongkai Mijovic, Vuksan Yang, Zijiang James Lou, Jian-Guang Zhang, Dongmei Xi'an Jiaotong University China Microsoft Research Asia China Shanghai Jiaotong University China Beihang University China Microsoft

ISBN: (纸本)9798350301298

Conditional graphic layout generation, which generates realistic layouts according to user constraints, is a challenging task that has not been well-studied yet. First, there is limited discussion about how to handle diverse user constraints flexibly and uniformly. Second, to make the layouts conform to user constraints, existing work often sacrifices generation quality significantly. In this work, we propose LayoutFormer++ to tackle the above problems. First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format. Then, we formulate conditional layout generation as a sequence-to-sequence transformation, and leverage encoder-decoder framework with Transformer as the basic architecture. Furthermore, to make the layout better meet user requirements without harming quality, we propose a decoding space restriction strategy. Specifically, we prune the predicted distribution by ignoring the options that definitely violate user constraints and likely result in low-quality layouts, and make the model samples from the restricted distribution. Experiments demonstrate that LayoutFormer++ outperforms existing approaches on all the tasks in terms of both better generation quality and less constraint violation. © 2023 ieee.

关键词： vision applications and systems

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Perfusion assessment via local remote photoplethysmography (rPPG)

Perfusion assessment via local remote photoplethysmography (...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kossack, Benjamin Wisotzky, Eric Eisert, Peter Schraven, Sebastian P. Globke, Brigitta Hilsmann, Anna Fraunhofer Heinrich Hertz Inst Berlin Germany Humboldt Univ Berlin Germany Rostock Univ Dept Otorhinolaryngol Head & Neck Surg Otto Korne Med Ctr Rostock Germany Charite Univ Med Berlin Dept Surg Berlin Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

关键词： Visualization Surgery Authentication Photoplethysmography Skin pattern recognition Monitoring

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Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

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