IEEE transactions on artificial intelligence最新文献_第4页

Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE transactions on artificial intelligence Pub Date : 2024-09-06 DOI: 10.1109/TAI.2024.3455311

Dengyong Zhang;Ruiyi He;Xin Liao;Feng Li;Jiaxin Chen;Gaobo Yang

{"title":"Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency","authors":"Dengyong Zhang;Ruiyi He;Xin Liao;Feng Li;Jiaxin Chen;Gaobo Yang","doi":"10.1109/TAI.2024.3455311","DOIUrl":"https://doi.org/10.1109/TAI.2024.3455311","url":null,"abstract":"Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"144-158"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach

IEEE transactions on artificial intelligence Pub Date : 2024-09-06 DOI: 10.1109/TAI.2024.3455313

Leena Heistrene;Juri Belikov;Dmitry Baimel;Liran Katzir;Ram Machlev;Kfir Levy;Shie Mannor;Yoash Levron

{"title":"An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach","authors":"Leena Heistrene;Juri Belikov;Dmitry Baimel;Liran Katzir;Ram Machlev;Kfir Levy;Shie Mannor;Yoash Levron","doi":"10.1109/TAI.2024.3455313","DOIUrl":"https://doi.org/10.1109/TAI.2024.3455313","url":null,"abstract":"Forecasting errors in power markets, even as small as 1%, can have significant financial implications. However, even high-performance artificial intelligence (AI) based electricity price forecasting (EPF) models have instances when their prediction error is much higher than those shown by mean performance metrics. To date, explainable AI has been used to enhance the model transparency and trustworthiness of AI-based EPF models. However, this article demonstrates that insights from explainable AI (XAI) techniques can be expanded beyond its primary task of explanatory visualizations. This work presents a XAI-based error compensation approach to improve model performance and identify irregular predictions. The first phase of the proposed approach involves error quantification through a Shapley additive explanations (SHAP) based corrector model that fine-tunes the base predictor's forecasts. Using this corrector model's SHAP explanations, the proposed approach distinguishes high-accuracy predictions from lower ones in the second stage. Additionally, these explanations are more simplified than the base model, making them easier for nonexpert users such as bidding agents. Performance enhancement and insightful user-centric explanations are crucial for real-world scenarios such as price spikes during network congestion, high renewable penetration, and fluctuating fuel costs. Case studies discussed here show the efficacy of the proposed approach independent of model architecture, feature combination, or behavioral patterns of electricity prices in different markets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"159-168"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Direct Adversarial Latent Estimation to Evaluate Decision Boundary Complexity in Black Box Models

IEEE transactions on artificial intelligence Pub Date : 2024-09-06 DOI: 10.1109/TAI.2024.3455308

Ashley S. Dale;Lauren Christopher

{"title":"Direct Adversarial Latent Estimation to Evaluate Decision Boundary Complexity in Black Box Models","authors":"Ashley S. Dale;Lauren Christopher","doi":"10.1109/TAI.2024.3455308","DOIUrl":"https://doi.org/10.1109/TAI.2024.3455308","url":null,"abstract":"A trustworthy artificial intelligence (AI) model should be robust to perturbed data, where robustness correlates with the dimensionality and linearity of feature representations in the model latent space. Existing methods for evaluating feature representations in the latent space are restricted to white-box models. In this work, we introduce \u0000<italic>direct adversarial latent estimation\u0000 (DALE) for evaluating the robustness of feature representations and decision boundaries for target black-box models. A surrogate latent space is created using a variational autoencoder (VAE) trained on a disjoint dataset from an object classification backbone, then the VAE latent space is traversed to create sets of adversarial images. An object classification model is trained using transfer learning on the VAE image reconstructions, then classifies instances in the adversarial image set. We propose that the number of times the classification changes in an image set indicates the complexity of the decision boundaries in the classifier latent space; more complex decision boundaries are found to be more robust. This is confirmed by comparing the DALE distributions to the degradation of the classifier F1 scores in the presence of adversarial attacks. This work enables the first comparisons of latent-space complexity between black box models by relating model robustness to complex decision boundaries.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6043-6053"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AugDiff: Diffusion-Based Feature Augmentation for Multiple Instance Learning in Whole Slide Image

IEEE transactions on artificial intelligence Pub Date : 2024-09-05 DOI: 10.1109/TAI.2024.3454591

Zhuchen Shao;Liuxi Dai;Yifeng Wang;Haoqian Wang;Yongbing Zhang

{"title":"AugDiff: Diffusion-Based Feature Augmentation for Multiple Instance Learning in Whole Slide Image","authors":"Zhuchen Shao;Liuxi Dai;Yifeng Wang;Haoqian Wang;Yongbing Zhang","doi":"10.1109/TAI.2024.3454591","DOIUrl":"https://doi.org/10.1109/TAI.2024.3454591","url":null,"abstract":"Multiple instance learning (MIL), a powerful strategy for weakly supervised learning, is able to perform various prediction tasks on gigapixel whole slide images (WSIs). However, the tens of thousands of patches in WSIs usually incur a vast computational burden for image augmentation, limiting the performance improvement in MIL. Currently, the feature augmentation-based MIL framework is a promising solution, while existing methods such as mixup often produce unrealistic features. To explore a more efficient and practical augmentation method, we introduce the diffusion model (DM) into MIL for the first time and propose a feature augmentation framework called AugDiff. The diverse generation capabilities of DM guarantee a various range of feature augmentations, while its iterative generation approach effectively preserves semantic integrity during these augmentations. We conduct extensive experiments over four distinct cancer datasets, two different feature extractors, and three prevalent MIL algorithms to evaluate the performance of AugDiff. Ablation study and visualization further verify the effectiveness. Moreover, we highlight AugDiff's higher quality augmented feature over image augmentation and its superiority over self-supervised learning. The generalization over external datasets indicates its broader applications. The code is open-sourced on \u0000<uri>https://github.com/szc19990412/AugDiff</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6617-6628"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring

IEEE transactions on artificial intelligence Pub Date : 2024-09-05 DOI: 10.1109/TAI.2024.3454566

Kristina Telegraph;Christos Kyrkou

引用次数: 0

Spiking Diffusion Models

IEEE transactions on artificial intelligence Pub Date : 2024-09-04 DOI: 10.1109/TAI.2024.3453229

Jiahang Cao;Hanzhong Guo;Ziqing Wang;Deming Zhou;Hao Cheng;Qiang Zhang;Renjing Xu

{"title":"Spiking Diffusion Models","authors":"Jiahang Cao;Hanzhong Guo;Ziqing Wang;Deming Zhou;Hao Cheng;Qiang Zhang;Renjing Xu","doi":"10.1109/TAI.2024.3453229","DOIUrl":"https://doi.org/10.1109/TAI.2024.3453229","url":null,"abstract":"Recent years have witnessed spiking neural networks (SNNs) gaining attention for their ultra-low energy consumption and high biological plausibility compared with traditional artificial neural networks (ANNs). Despite their distinguished properties, the application of SNNs in the computationally intensive field of image generation is still under exploration. In this article, we propose the spiking diffusion models (SDMs), an innovative family of SNN-based generative models that excel in producing high-quality samples with significantly reduced energy consumption. In particular, we propose a temporal-wise spiking mechanism (TSM) that allows SNNs to capture more temporal features from a bio-plasticity perspective. In addition, we propose a threshold-guided strategy that can further improve the performances by up to 16.7% without any additional training. We also make the first attempt to use the ANN-SNN approach for SNN-based generation tasks. Extensive experimental results reveal that our approach not only exhibits comparable performance to its ANN counterpart with few spiking time steps, but also outperforms previous SNN-based generative models by a large margin. Moreover, we also demonstrate the high-quality generation ability of SDM on large-scale datasets, e.g., LSUN bedroom. This development marks a pivotal advancement in the capabilities of SNN-based generation, paving the way for future research avenues to realize low-energy and low-latency generative applications.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"132-143"},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives 通过对约束条件和目标的松弛实现约束多目标优化

IEEE transactions on artificial intelligence Pub Date : 2024-09-04 DOI: 10.1109/TAI.2024.3454025

Fei Ming;Bing Xue;Mengjie Zhang;Wenyin Gong;Huixiang Zhen

{"title":"Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives","authors":"Fei Ming;Bing Xue;Mengjie Zhang;Wenyin Gong;Huixiang Zhen","doi":"10.1109/TAI.2024.3454025","DOIUrl":"https://doi.org/10.1109/TAI.2024.3454025","url":null,"abstract":"Since most multiobjective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multiobjective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6709-6722"},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Q-Cogni: An Integrated Causal Reinforcement Learning Framework

IEEE transactions on artificial intelligence Pub Date : 2024-09-03 DOI: 10.1109/TAI.2024.3453230

Cristiano da Costa Cunha;Wei Liu;Tim French;Ajmal Mian

{"title":"Q-Cogni: An Integrated Causal Reinforcement Learning Framework","authors":"Cristiano da Costa Cunha;Wei Liu;Tim French;Ajmal Mian","doi":"10.1109/TAI.2024.3453230","DOIUrl":"https://doi.org/10.1109/TAI.2024.3453230","url":null,"abstract":"We present \u0000<italic>Q-Cogni\u0000, an algorithmically integrated causal reinforcement learning framework that redesigns \u0000<italic>Q-Learning\u0000 to improve the learning process with causal inference. \u0000<italic>Q-Cogni\u0000 achieves improved policy quality and learning efficiency with a prelearned structural causal model of the environment, queried to guide the policy learning process with an understanding of cause-and-effect relationships in a state-action space. By doing so, we not only leverage the sample efficient techniques of reinforcement learning but also enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply \u0000<italic>Q-Cogni\u0000 on vehicle routing problem (VRP) environments including a real-world dataset of taxis in New York City using the Taxi and Limousine Commission trip record data. We show \u0000<italic>Q-Cogni's\u0000 capability to achieve an optimally guaranteed policy (total trip distance) in 76% of the cases when comparing to shortest-path-search methods and outperforming (shorter distances) state-of-the-art reinforcement learning algorithms in 66% of cases. Additionally, since \u0000<italic>Q-Cogni\u0000 does not require a complete global map, we show that it can start efficiently routing with partial information and improve as more data is collected, such as traffic disruptions and changes in destination, making it ideal for deployment in real-world dynamic settings.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6186-6195"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning Over Directed Graphs

IEEE transactions on artificial intelligence Pub Date : 2024-08-30 DOI: 10.1109/TAI.2024.3452678

Yifan Hu;Junjie Fu;Guanghui Wen;Changyin Sun

{"title":"Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning Over Directed Graphs","authors":"Yifan Hu;Junjie Fu;Guanghui Wen;Changyin Sun","doi":"10.1109/TAI.2024.3452678","DOIUrl":"https://doi.org/10.1109/TAI.2024.3452678","url":null,"abstract":"Learning efficient coordination policies over continuous state and action spaces remains a huge challenge for existing distributed multi-agent reinforcement learning (MARL) algorithms. In this article, the classic deterministic policy gradient (DPG) method is extended to the distributed MARL domain to handle the continuous control policy learning issue for a team of homogeneous agents connected through a directed graph. A theoretical on-policy distributed actor–critic algorithm is first proposed based on a local DPG theorem, which considers observation-based policies, and incorporates consensus updates for the critic and actor parameters. Stochastic approximation theory is then used to obtain asymptotic convergence results of the algorithm under standard assumptions. Thereafter, a practical distributed deterministic actor–critic algorithm is proposed by integrating the theoretical algorithm with the deep reinforcement learning training architecture, which achieves better scalability, exploration ability, and data efficiency. Simulations are carried out in standard MARL environments with continuous action spaces, where the results demonstrate that the proposed distributed algorithm achieves comparable learning performance to solid centralized trained baselines while demanding much less communication resources.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"118-131"},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FIMKD: Feature-Implicit Mapping Knowledge Distillation for RGB-D Indoor Scene Semantic Segmentation

IEEE transactions on artificial intelligence Pub Date : 2024-08-30 DOI: 10.1109/TAI.2024.3452052

Wujie Zhou;Yuxiang Xiao;Yuanyuan Liu;Qiuping Jiang

{"title":"FIMKD: Feature-Implicit Mapping Knowledge Distillation for RGB-D Indoor Scene Semantic Segmentation","authors":"Wujie Zhou;Yuxiang Xiao;Yuanyuan Liu;Qiuping Jiang","doi":"10.1109/TAI.2024.3452052","DOIUrl":"https://doi.org/10.1109/TAI.2024.3452052","url":null,"abstract":"Depth images are often used to improve the geometric understanding of scenes owing to their intuitive distance properties. Although there have been significant advancements in semantic segmentation tasks using red–green–blue-depth (RGB-D) images, the complexity of existing methods remains high. Furthermore, the requirement for high-quality depth images increases the model inference time, which limits the practicality of these methods. To address this issue, we propose a feature-implicit mapping knowledge distillation (FIMKD) method and a cross-modal knowledge distillation (KD) architecture to leverage deep modal information for training and reduce the model dependence on this information during inference. The approach comprises two networks: FIMKD-T, a teacher network that uses RGB-D data, and FIMKD-S, a student network that uses only RGB data. FIMKD-T extracts high-frequency information using the depth modality and compensates for the loss of RGB details due to a reduction in resolution during feature extraction by the high-frequency feature enhancement module, thereby enhancing the geometric perception of semantic features. In contrast, the FIMKD-S network does not employ deep learning techniques; instead, it uses a nonlearning approach to extract high-frequency information. To enable the FIMKD-S network to learn deep features, we propose a feature-implicit mapping KD for feature distillation. This mapping technique maps the features in channel and space to a low-dimensional hidden layer, which helps to avoid inefficient single-pattern student learning. We evaluated the proposed FIMKD-S* (FIMKD-S with KD) on the NYUv2 and SUN-RGBD datasets. The results demonstrate that both FIMKD-T and FIMKD-S* achieve state-of-the-art performance. Furthermore, FIMKD-S* provides the best performance balance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6488-6499"},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0