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PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection

PUDD: Towards Robust Multi-modal Prototype-based Deepfake De...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alvaro Lopez Pellcier Yi Li Plamen Angelov School of Computing and Communications Lancaster University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learning-based deepfake detection methods, particularly with diffusion models, have shown remarkable progress. However, there is a growing demand for real-world applications to detect unseen individuals, deepfake techniques, and scenarios. To address this limitation, we propose a Prototype-based Unified Framework for Deepfake Detection (PUDD). PUDD offers a detection system based on similarity, comparing input data against known prototypes for video classification and identifying potential deepfakes or previously unseen classes by analyzing drops in similarity. Our extensive experiments reveal three key findings: (1) PUDD achieves an accuracy of 95.1% on Celeb-DF, outperforming state-of-the-art deepfake detection methods; (2) PUDD leverages image classification as the upstream task during training, demonstrating promising performance in both image classification and deepfake detection tasks during inference; (3) PUDD requires only 2.7 seconds for retraining on new data and emits 10 5 times less carbon compared to the state-of-the-art model, making it significantly more environmentally friendly.

关键词： Training Deepfakes computer vision conferences Computational modeling Prototypes Diffusion models

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Fully Test-time Adaptation for Object Detection

Fully Test-time Adaptation for Object Detection

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiaoqian Ruan Wei Tang University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://***/XiaoqianRuan1/IoU-filter.

关键词： Training computer vision Codes conferences Training data Detectors Object detection

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Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook

Scaling up biologically-inspired computer vision: A case stu...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nicolas Pinto Zak Stone Todd Zickler David Cox McGovern Institute of Brain Research MIT Cambridge MA USA Rowland Institute at Harvard Harvard University Cambridge MA USA School of Engineering and Applied Sciences Harvard University Cambridge MA USA

Biological visual systems are currently unrivaled by artificial systems in their ability to recognize faces and objects in highly variable and cluttered real-world environments. Biologically-inspired computer vision systems seek to capture key aspects of the computational architecture of the brain, and such approaches have proven successful across a range of standard object and face recognition tasks (e.g. [23, 8, 9, 18]). Here, we explore the effectiveness of these algorithms on a large-scale unconstrained real-world face recognition problem based on images taken from the Face-book social networking website. In particular, we use a family of biologically-inspired models derived from a high-throughput feature search paradigm [19, 15] to tackle a face identification task with up to one hundred individuals (a number that approaches the reasonable size of real-world social networks). We show that these models yield high levels of face-identification performance even when large numbers of individuals are considered; this performance increases steadily as more examples are used, and the models outperform a state-of-the-art commercial face recognition system. Finally, we discuss current limitations and future opportunities associated with datasets such as these, and we argue that careful creation of large sets is an important future direction.

关键词： Face Facebook Face recognition Biological system modeling Brain models

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Medium level scene representation using a VLSI smart hexagonal sensor with multiresolution edge extraction capability and scale space integration processing

Medium level scene representation using a VLSI smart hexagon...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tremblay Savard Poussart Computer Vision and Digital Systems Laboratory Department of Electrical Engineering Laval University QUE Canada

This paper presents a sensing approach where photo-transduction, multi-resolution feature extraction, scale-space integration and edge tracking are performed on a mixed (digital-analog) VLSI architecture in order to generate medium-level scene description. The proposed system is mainly targeted for robot vision applications where feature description is preferred to a set of raw or raster 2D images and edge maps. The Multiport Access photo-Receptor (MAR) is a CMOS sensor and represents the main sensory part of this integrated image acquisition system. VLSI also provides means to integrate analog computing, digital controller and DSP co-processor modules which define a powerful sensory chip set for focal plane image processing. A current version of the MAR sensor which implements 256/spl times/256 pixels includes 16 analog spatial filters which simultaneously compute multiresolution edge maps. This unique 2D hexagonal smart sensor approach which performs up to 8.5/spl times/10/sup 9/ arithmetic Op/sec during the acquisition/filtering phase and 25/spl times/10/sup 9/ Logical Op/sec for scale-space integration allows high resolution image capability. It represents a significant improvement for passive sensory units in a compact assembly for computer vision applications.< >

关键词： Very-large-scale integration Image edge analysis Feature extraction Robots, vision systems Spatial filters CMOS integrated circuits Image sensors

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Cross-view Aggregation Network For Stereo Image Super-Resolution

Cross-view Aggregation Network For Stereo Image Super-Resolu...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhitao Chen Tao Lu Kanghui Zhao Bolin Zhu Zhen Li Jiaming Wang Yanduo Zhang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although stereo image super-resolution has been extensively studied, many existing works only rely on attention in a single epipolar direction to reconstruct stereo images. In the case of asymmetric parallax images, these methods often struggle to capture reliable stereo correspondence, resulting in reconstructed images suffering from blurring and artifacts. In this paper, we propose a novel method called Cross-View Aggregation Network for Stereo Image Super-Resolution (CANSSR) and explore the relationship between multi-directional epipolar lines to construct reliable stereo correspondence. Specifically, we propose a multidirectional cross-view aggregation module (MCAM) that effectively captures multi-directional stereo correspondence and obtains cross-view complementary information. Furthermore, we design a channel-spatial aggregation module (CSAM) that aggregates multi-order global-local information in intra-view to reconstruct clearer texture features. In addition, we equip a large kernel convolution in the Feedforward Network to acquire richer detailed texture information. The extensive experiments conclusively demonstrate that CANSSR outperforms the state-of-the-art method both qualitatively and quantitatively in terms of stereo image super-resolution on the Flickr 1024 and Middlebury datasets.

关键词： Image databases Convolution computer network reliability Superresolution pattern recognition Reliability Web sites

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Facial Expression recognition using Auto-regressive Models

Facial Expression Recognition using Auto-regressive Models

引用

International conference on pattern recognition

作者： F. Dornaika F. Davoine Computer Vision Center Barcelona Spain HEUDIASYC Mixed Research Unit CNRS/UTC Compiegne France

In this paper, we address the analysis and recognition of facial expressions in continuous videos. We introduce a view- and texture-independent approach that exploits the temporal facial action parameters estimated by an appearance-based 3D face tracker. The facial expression recognition is carried out using learned dynamical models based on auto-regressive processes. These learned models can also be utilized for the synthesis and prediction tasks. Experiments demonstrated the effectiveness of the developed method

关键词： Face recognition Shape Eyebrows Emotion recognition Deformable models Hidden Markov models Head computer vision Videos Parameter estimation

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcus Valtonen Ö rnhag Patrik Persson Må rten Wadenbä ck Kalle Å strö m Anders Heyden Centre for Mathematical Sciences Lund University Department of Electrical Engineering Link&#x00F6 ping University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated *** proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://***/marcusvaltonen/DronePoseLib. 1

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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