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Computer vision - ECCV 2022 . Part V : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022 : proceedings / / Shai Avidan [and four others]
| Computer vision - ECCV 2022 . Part V : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022 : proceedings / / Shai Avidan [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (804 pages) |
| Disciplina | 006.37 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision - Equipment and supplies
Pattern recognition systems - Data processing |
| ISBN | 3-031-20065-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Organization -- Contents - Part V -- Adaptive Image Transformations for Transfer-Based Adversarial Attack -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Attack -- 2.2 Adversarial Defense -- 3 Method -- 3.1 Notations -- 3.2 Overview of AITL -- 3.3 Training AITL -- 3.4 Generating Adversarial Examples with AITL -- 4 Experiments -- 4.1 Settings -- 4.2 Compared with the State-of-the-Art Methods -- 4.3 Analysis on Image Transformation Operations -- 5 Conclusion -- References -- Generative Multiplane Images: Making a 2D GAN 3D-Aware -- 1 Introduction -- 2 Related Work and Background -- 3 Generative Multiplane Images (GMPI) -- 3.1 Overview -- 3.2 GMPI: StyleGANv2 with an Alpha Branch -- 3.3 Differentiable Rendering in GMPI -- 3.4 Discriminator Pose Conditioning -- 3.5 Miscellaneous Adjustment: Shading-Guided Training -- 3.6 Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Results -- 4.4 Ablation Studies -- 5 Conclusion -- References -- AdvDO: Realistic Adversarial Attacks for Trajectory Prediction -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation and Challenges -- 4 AdvDO: Adversarial Dynamic Optimization -- 4.1 Dynamic Parameters Estimation -- 4.2 Adversarial Trajectory Generation -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Main Results -- 6 Conclusion -- References -- Adversarial Contrastive Learning via Asymmetric InfoNCE -- 1 Introduction -- 2 Asymmetric InfoNCE -- 2.1 Notations -- 2.2 Asymmetric InfoNCE: A Generic Learning Objective -- 3 Adversarial Asymmetric Contrastive Learning -- 3.1 Adversarial Samples as Inferior Positives -- 3.2 Adversarial Samples as Hard Negatives -- 4 Experiments -- 4.1 Main Results -- 4.2 Transferring Robust Features -- 4.3 Ablation Studies -- 4.4 Qualitative Analysis -- 5 Related Work -- 6 Conclusions -- References.
One Size Does NOT Fit All: Data-Adaptive Adversarial Training -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Defense -- 2.2 Decline of the Natural Accuracy -- 3 Review of Standard Adversarial Training -- 4 Adversarial Perturbation Size Matters -- 5 Data-Adaptive Adversarial Training -- 6 Experiment -- 6.1 Investigation of Hyper-parameters -- 6.2 White-Box and Black-Box Attacks -- 6.3 Distribution of Adversaries -- 6.4 DAAT with Different Calibration Strategies -- 7 Conclusions -- References -- UniCR: Universally Approximated Certified Robustness via Randomized Smoothing -- 1 Introduction -- 2 Universally Approximated Certified Robustness -- 2.1 Universal Certified Robustness -- 2.2 Approximating Tight Certified Robustness -- 3 Deriving Certified Radius Within Robustness Boundary -- 3.1 Calculating Certified Radius in Practice -- 4 Optimizing Noise PDF for Certified Robustness -- 4.1 Noise PDF Optimization -- 4.2 C-OPT and I-OPT -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Universality Evaluation -- 5.3 Optimizing Certified Radius with C-OPT -- 5.4 Optimizing Certified Radius with I-OPT -- 5.5 Best Performance Comparison -- 6 Related Work -- 7 Conclusion -- References -- Hardly Perceptible Trojan Attack Against Neural Networks with Bit Flips -- 1 Introduction -- 2 Related Works and Preliminaries -- 3 Methodology -- 3.1 Hardly Perceptible Trigger -- 3.2 Problem Formulation -- 3.3 Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Attack Results -- 4.3 Human Perceptual Study -- 4.4 Discussions -- 4.5 Potential Defenses -- 4.6 Ablation Studies -- 5 Conclusion -- References -- Robust Network Architecture Search via Feature Distortion Restraining -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Method -- 4.1 Network Vulnerability Constraint -- 4.2 Robust Network Architecture Search (RNAS) -- 5 Experiment -- 5.1 Experimental Setup. 5.2 Results on CIFAR-10 -- 5.3 Results on CIFAR-100, SVHN and Tiny-ImageNet -- 5.4 Upper Bound H of network vulnerability -- 6 Conclusion -- References -- SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Method Overview -- 3.3 Generation Backbone of SecretGen -- 3.4 Pseudo Label Predictor -- 3.5 Latent Vector Selector -- 3.6 SecretGen Optimization -- 3.7 Discussion -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Protocols -- 4.3 Attack Performance -- 4.4 Robustness Evaluation -- 4.5 Ablation Studies -- 5 Conclusion -- References -- Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Motivation -- 3.3 Triangle Attack -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation on Standard Models -- 4.3 Evaluation on Real-world Applications -- 4.4 Ablation Study -- 4.5 Further Discussion -- 5 Conclusion -- References -- Data-Free Backdoor Removal Based on Channel Lipschitzness -- 1 Introduction -- 2 Related Work -- 2.1 Backdoor Attack -- 2.2 Backdoor Defense -- 3 Preliminaries -- 3.1 Notations -- 3.2 L-Lipschitz Function -- 3.3 Lipschitz Constant in Neural Networks -- 4 Methodology -- 4.1 Channel Lipschitz Constant -- 4.2 Trigger-Activated Change -- 4.3 Correlation Between CLC and TAC -- 4.4 Special Case in CNN with Batch Normalization -- 4.5 Channel Lipschitzness Based Pruning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Studies -- 6 Conclusions -- References -- Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack -- 1 Introduction -- 2 Background and Notions -- 3 Method -- 3.1 A CAM-driven Erasing Strategy -- 3.2 A Random-Erasing-Based Self-KD Module. 4 Experiments -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 5 Conclusion -- References -- Learning Energy-Based Models with Adversarial Training -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Energy-Based Models -- 3.2 Binary Adversarial Training -- 4 Binary at Generative Model -- 4.1 Optimal Solution to the Binary at Problem -- 4.2 Learning Mechanism -- 4.3 Maximum Likelihood Learning Interpretation -- 4.4 Improved Training of Binary at Generative Model -- 5 Experiments -- 5.1 Image Generation -- 5.2 Applications -- 5.3 Training Stability Analysis -- 6 Conclusion -- References -- Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Graph Convolution Network and Its Decoupled Variants -- 2.2 Label Propagation and Its Role in GCN -- 2.3 Adversarial Poisoning Attacks on Graphs -- 3 Label Poisoning Attack Model -- 3.1 Label Propagation -- 3.2 Maximum Gradient Attack -- 3.3 GCN Training with Poisoned Labels -- 4 Experiments -- 5 Attack Performance Evaluation -- 6 Conclusion -- References -- Revisiting Outer Optimization in Adversarial Training -- 1 Introduction -- 2 Analyzing Outer Optimization in AT -- 2.1 Notations -- 2.2 Comparison of Gradient Properties -- 2.3 Revisiting Stochastic Gradient Descent -- 2.4 Example-Normalized Gradient Descent with Momentum -- 2.5 Accelerating ENGM via Gradient Norm Approximation -- 3 Experiments and Analysis -- 3.1 Comparison of Optimization Methods -- 3.2 Combination with Benchmark at Methods -- 3.3 Comparison with SOTA -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Zero-Shot Attribute Attacks on Fine-Grained Recognition Models -- 1 Introduction -- 2 Related Works -- 3 Fine-Grained Compositional Adversarial Attacks -- 3.1 Problem Setting. 3.2 Compositional Attribute-Based Universal Perturbations (CAUPs) -- 3.3 Learning AUPs -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusions -- References -- Towards Effective and Robust Neural Trojan Defenses via Input Filtering -- 1 Introduction -- 2 Standard Trojan Attack -- 3 Difficulty in Finding Input-Specific Triggers -- 4 Proposed Trojan Defenses -- 4.1 Variational Input Filtering -- 4.2 Adversarial Input Filtering -- 4.3 Filtering Then Contrasting -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results of Baseline Defenses -- 5.3 Results of Proposed Defenses -- 5.4 Ablation Studies -- 6 Related Work -- 7 Conclusion -- References -- Scaling Adversarial Training to Large Perturbation Bounds -- 1 Introduction -- 2 Related Works -- 3 Preliminaries and Threat Model -- 3.1 Notation -- 3.2 Nomenclature of Adversarial Attacks -- 3.3 Objectives of the Proposed Defense -- 4 Proposed Method -- 5 Analysing Oracle Alignment of Adversarial Attacks -- 6 Role of Image Contrast in Robust Evaluation -- 7 Experiments and Results -- 8 Conclusions -- References -- Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack -- 1 Introduction -- 1.1 Related Works -- 2 Background -- 3 Theoretical and Empirical Study on the Landscape of the Adversarial Loss -- 4 The BABIES Algorithm -- 5 Experimental Evaluation -- 5.1 Results of BABIES on MNIST, CIFAR-10 and ImageNet -- 5.2 Results on Attacking Google Cloud Vision -- 6 Discussion and Conclusion -- References -- Generative Domain Adaptation for Face Anti-Spoofing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Generative Domain Adaptation -- 3.3 Overall Objective and Optimization -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparisons to the State-of-the-Art Methods -- 4.3 Ablation Studies. 4.4 Visualization and Analysis. |
| Record Nr. | UNISA-996500066703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computer Vision - ECCV 2022 . Part VIII : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, proceedings / / Shai Avidan [and four others]
| Computer Vision - ECCV 2022 . Part VIII : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, proceedings / / Shai Avidan [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (807 pages) |
| Disciplina | 006.4 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Pattern recognition systems
Pattern recognition systems - Data processing |
| ISBN | 3-031-20074-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996500064103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mathematical foundations of image processing and analysis 1 / / Jean-Charles Pinoli
| Mathematical foundations of image processing and analysis 1 / / Jean-Charles Pinoli |
| Autore | Pinoli Jean-Charles |
| Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (456 p.) |
| Disciplina | 621.367 |
| Collana | Digital Signal and Image Processing Series |
| Soggetto topico |
Image processing - Digital techniques
Pattern recognition systems - Data processing Image analysis - Data processing |
| ISBN |
1-118-62570-6
1-118-64911-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Introduction; Elements of Mathematical Terminology; Part 1. An Overview of Image Processing and Analysis (IPA); Chapter 1. Gray-Tone Images; 1.1. Intensity images, pixels and gray tones; 1.2. Scene, objects, context, foreground and background; 1.3. Simple intensity image formation process models; 1.3.1. The multiplicative image formation process model; 1.3.2. The main human brightness perception laws; 1.4. The five main requirements for a relevant imaging approach; 1.5. Additional comments; Chapter 2. Gray-Tone Image Processing and Analysis
2.1. Image processing2.1.1. Image enhancement; 2.1.2. Image restoration; 2.1.3. Image inpainting; 2.1.4. Image warping, registration and morphing; 2.2. Image analysis; 2.2.1. Image features; 2.2.2. Image feature detection and extraction; 2.2.3. Image segmentation; 2.3. Image comparison; 2.3.1. Image pattern analysis, recognition and formation; 2.3.2. Image quality measure; 2.4. Importance of Human Vision; 2.5. Additional comments; Chapter 3. Binary Images; 3.1. Scene, objects and context; 3.1.1. Types of collection of objects; 3.1.2. Types of perturbations; 3.2. Binary and multinary images 3.2.1. Binary images3.2.2. Multinary images; 3.3. Additional comments; Chapter 4. Binary Image Processing and Analysis; 4.1. Binary image processing; 4.1.1. Binary image processing methods; 4.2. Binary image analysis; 4.2.1. Object feature detection and extraction; 4.3. Binary image and object description; 4.3.1. Binary image and object descriptors; 4.3.2. Properties of the binary image and object descriptor; 4.4. Object comparison; 4.5. Object analysis, recognition and formation; 4.5.1. Object recognition; 4.5.2. Object formation; 4.6. Additional comments Chapter 5. Key Concepts and Notions for IPA5.1. Dimensionality; 5.1.1. Dimension in Physics; 5.1.2. Dimension in Mathematics; 5.1.3. Dimension in imaging sciences and technologies; 5.2. Continuity and discreteness; 5.3. Scale, resolution and definition; 5.3.1. Scale; 5.3.2. Resolution; 5.3.3. Image definition; 5.4. Domains; 5.5. Ranges; 5.5.1. Pointwise ranges; 5.5.2. Local ranges; 5.5.3. Global ranges; 5.5.4. Constrained ranges; 5.6. Additional comments; Chapter 6. Mathematical Imaging Frameworks; 6.1. Mathematical imaging frameworks; 6.1.1. Mathematical imaging paradigms 6.1.2. Mathematical imaging frameworks6.1.3. Mathematical imaging approaches; 6.2. Image representation and image modeling; 6.2.1. Imaging representation; 6.2.2. Imaging modeling; 6.3. A mathematical imaging methodology; 6.4. Additional comments; Part 2. Basic Mathematical Reminders for Gray-Tone and Binary Image Processing and Analysis; Chapter 7. Basic Reminders in Set Theory; 7.1. Mathematical disciplines; 7.2. Sets and elements; 7.2.1. Membership; 7.2.2. Relations and operations between sets; 7.2.3. Power sets; 7.3. Order and equivalence relations on sets; 7.3.1. Order relations on sets 7.3.2. Lattices and complete lattices |
| Record Nr. | UNINA-9910132333903321 |
Pinoli Jean-Charles
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| London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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