Adversarial Multimedia Forensics |
Autore | Nowroozi Ehsan |
Edizione | [1st ed.] |
Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
Descrizione fisica | 1 online resource (298 pages) |
Altri autori (Persone) |
KallasKassem
JolfaeiAlireza |
Collana | Advances in Information Security Series |
ISBN | 3-031-49803-8 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- About the Editors -- Model Poisoning Attack Against Federated Learning with Adaptive Aggregation -- 1 Introduction -- 2 Related Work -- 2.1 Federated Learning in Image Classification -- 2.2 Poisoning Attacks in Federated Learning -- 2.3 Adaptive Aggregation in Federated Learning -- 3 Background and Problem Definition -- 4 Stealthy Model Poisoning Attack (MSA) -- 5 Adaptive Federated Optimization -- 6 Evaluation -- 6.1 Simulation Setup -- 6.2 Parameter and System Setting -- 6.3 Test Metrics -- 6.4 Dataset -- 7 Experimental Results -- 7.1 Results in No Attack Scenario -- 7.2 Results With Attack in Rounds Multiples of 5 -- 7.3 Results With Attack in rounds multiples of 10 -- 7.4 Results With Attack in Rounds Multiples of 20 -- 8 Discussion on the Results -- 9 Conclusions and Future Work -- References -- Image Forgery Detection Using Comprint: A Comprehensive Study -- 1 Introduction -- 2 State of the Art -- 2.1 Active Forgery Detection Methods -- 2.2 Passive Forgery Detection Methods -- 3 Comprint -- 3.1 Fingerprint Extraction -- 3.2 Forgery Localization -- 3.3 Fusing Comprint and Noiseprint -- 4 Evaluation -- 4.1 Measures -- 4.2 Artificial Dataset: Differences in JPEG Parameters -- 4.3 Realistic Datasets: Real and Manipulated Images -- 5 Limitations -- 6 Conclusion -- References -- Good or Evil: Generative Adversarial Networks in Digital Forensics -- 1 Introduction -- 2 Background -- 2.1 Forensics vs Anti Forensics vs Counter-Anti Forensics -- 2.2 Generative Adversarial Networks -- 2.3 Methodology -- 3 GAN-Based Anti-forensics -- 3.1 Image Manipulation Anti-forensics -- 3.2 Anti-forensics for Computer-Generated Images -- 3.3 GAN-Based Audio Anti-forensics -- 3.4 Advantages of GAN-Based Anti-forensics -- 4 Overview of GAN Methods to Improve the Forensic Detectors.
4.1 Strengthening Forensic Detectors to Identify Deep Fakes -- 4.2 Improving Signature Forensics Using GANs -- 4.3 Summary of GAN Usage to Improve the Detection of Forged and Synthetic Data -- 5 Future Directions -- 6 Conclusion -- References -- Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Siamese Network Based Forensic Algorithms -- 2.2 Traditional Anti-Forensic Attacks -- 2.3 GAN-Based Approaches -- 2.4 Adversarial Example Attacks -- 2.5 Attacks Against Clustering and Contrastive Learning -- 2.6 Multi-Task Training Optimization -- 3 Problem Formulation -- 4 Proposed Method -- 4.1 Stage 1. Patch Sampling -- 4.2 Stage 2. Refinement -- Loss Functions -- Refinement Training -- Deployment -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Training Methods -- Construction of Communities -- Sampling from Communities -- Training Parameters -- 5.3 Evaluation Metrics -- 6 Results -- 6.1 Overall Attack Performance -- 6.2 When Baseline Generative Attack Fails -- Attack Against Splicing Detection -- Attack Against Splicing Localization -- 6.3 Ablation Study -- Impact of LDn -- Impact of LSn -- Impact of Multi-Optimizer -- 7 Limitations -- 7.1 Improvement from Good Baseline Generative Attacked Images -- 7.2 Uncontrollable Localization Heatmap -- 7.3 Optimal Loss Weights and Learning Rates -- 8 Conclusion -- References -- Generative Adversarial Networks for Artificial Satellite Image Creation and Manipulation -- 1 Introduction -- 2 Relevant Work -- 3 Datasets -- 4 DNN Models -- 4.1 Pix2Pix for Season Transfer -- Architecture -- Loss Function -- Training -- 4.2 CycleGAN for Land Cover Transfer -- CycleGAN Architecture -- Loss Function -- Training -- 4.3 Splicing wiht iGPT -- Architecture -- 4.4 Splicing Using Vision Transformer -- Architecture -- Training -- 5 Results -- 6 Conclusion. References -- Domain Specific Information Based Learning for Facial ImageForensics -- 1 Introduction -- 2 Multimedia Forensics -- 3 General Framework of Automated Forensic Face Recognition -- 4 Related Work -- 5 Challenges of Face Recognition for Forensics -- 6 Motivation -- 7 Datasets Used -- 7.1 Composite Sketch with Age Variations Dataset -- 7.2 E- PRIP Dataset -- 7.3 Disguised Faces in the Wild Dataset -- 8 Metrics Used to Evaluate the Performance -- 9 Applications -- 10 Conclusion -- References -- Linguistic Steganography and Linguistic Steganalysis -- 1 Introduction -- 2 Preliminaries -- 2.1 Mathematical Description of Linguistic Steganography -- 2.2 General Framework of Linguistic Steganalysis -- 2.3 Language Model -- 2.4 Evaluation Metrics -- 3 Linguistic Steganography -- 3.1 Modification-Oriented Linguistic Steganography (ModLS) -- 3.2 Generation-Oriented Linguistic Steganography (GenLS) -- 4 Linguistic Steganalysis -- 4.1 Conventional Linguistic Steganalysis -- 4.2 End-to-End Linguistic Steganalysis -- 5 Challenges and Opportunities -- 6 Conclusion -- References -- Random Deep Feature Selection's Efficiency in Securing Image Manipulation Detectors Opposed by Adversarial Attacks -- 1 Introduction -- 2 Proposed Method -- 2.1 RDFS Detection Considering FC Networks -- 2.2 RDFS Detection Considering SVM -- 3 Different Implementations of Manipulation Detection -- 3.1 CNN Original Detectors -- 3.2 Minimizing Detectors -- 3.3 Adversarial Attacks -- 4 Discussion and Experimental Findings -- 4.1 Experimental Design -- 4.2 Classification Based on FC Results -- 4.3 Classification Based on SVM Results -- 5 Conclusion -- References -- Optimized Distribution for Robust Watermarking of Deep Neural Networks Through Fixed Embedding Weights -- 1 Introduction -- 2 DNN Watermarking History and Prior Art -- 3 Notation, Watermarking Model and Requirements. 3.1 Preliminaries -- 3.2 Watermark Specifications and Model -- 4 Suggested DDN Watermarking Method -- 4.1 Reliable Watermarking Algorithm -- Watermark Embedding -- Optimizing the Distribution of Weights with Water Markers -- 4.2 Watermark Extraction -- 5 Experimental Methodology -- 5.1 Host Networks and Tasks -- 5.2 Watermarking Algorithm Configuration -- 5.3 Setting for Robustness Analysis -- 6 Findings and Analysis -- 6.1 Performance of Watermarked DNN Models -- Analysis of Weights Distribution (Watermark Security) -- 6.2 Robustness Assessment -- Model Compression -- Retraining -- Comparison with the State of the Art -- 7 Conclusion Stage -- References -- Anti Forensic Measures and Their Impact on Forensic Investigations -- 1 Introduction -- 2 Factors of AFM Effectiveness -- 2.1 Problems Regarding the Effectiveness -- 2.2 Discussed Factors -- 3 Anti Forensic Measures -- 3.1 Physical Destruction -- 3.2 Artifact Wiping -- 3.3 Disk Wiping -- 3.4 NSRL Scrubbing -- 3.5 MACE Scrambling -- 3.6 File Signature Scrambling -- 3.7 Registry Wiping -- 3.8 Log Manipulation -- 3.9 Defragmentation -- 3.10 Artifact Injection -- 3.11 File Compression -- 3.12 Forensic Tool Bug Exploitation -- 3.13 Compression Bombs -- 3.14 Loop References -- 3.15 Steganography -- 3.16 Data Hiding Techniques -- 3.17 Live Operating Systems -- 3.18 Virtual Machines -- 3.19 Non-standard Programs -- 3.20 Data Pooling -- 3.21 Non-standard RAIDs -- 3.22 Dummy Drives -- 3.23 Full Disk/Drive, and File Encryption -- 3.24 Encrypted Containers -- 3.25 File Encryption -- 3.26 Hash Collisions -- 3.27 Media Integrity Obfuscation -- 4 Conclusion -- References -- Using Vocoder Artifacts For Audio Deepfakes Detection -- 1 Introduction -- 2 Related Works on Audio Deepfake -- 2.1 Human Voice Synthesis -- 2.2 Neural Vocoders -- 2.3 Autoregressive Models -- 2.4 Diffusion Based Models. 2.5 GAN Based Models -- 2.6 Synthetic Audio Detection -- 3 Vocoder Detection -- 3.1 Speaker Verification Based Rawnet2 -- 3.2 Vocoder Identification Based Rawnet2 -- 4 The LibriVoc Datasets -- 4.1 Librivoc High-Level Overview -- 4.2 Training Process -- 4.3 Train/Dev/Test Split Configuration -- 5 Evaluation Results -- 5.1 Synthesized Audio Detection -- 5.2 Classification of Neural Vocoder Artifacts -- 5.3 Leave N Out Cross-Validation -- 5.4 Robustness and Ablation Studies -- 6 Conclusion -- References -- Index. |
Record Nr. | UNINA-9910842298403321 |
Nowroozi Ehsan
![]() |
||
Cham : , : Springer, , 2024 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
IInformation fusion in distributed sensor networks with Byzantines / / Andrea Abrardo [et al.] |
Autore | Abrardo Andrea |
Edizione | [1st edition 2021.] |
Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (XIX, 109 p. 26 illus., 15 illus. in color.) |
Disciplina | 006.25 |
Collana | Signals and Communication Technology |
Soggetto topico |
Signal processing
Image processing Speech processing systems Computer security System safety |
ISBN | 981-329-001-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- Preliminaries -- Security attacks and defenses in distributed sensor networks -- A Heuristic defense: A soft isolation algorithm for adversarial decision fusion -- Optimum decision fusion in the presence of Byzantines -- Nearly optimum decision fusion via message passing -- Decision fusion of hidden-Markov observations with synchronized attacks -- Decision fusion with unbalanced priors -- Decision fusion game with incomplete knowledge in a Bayesian setup -- Conclusions. |
Record Nr. | UNINA-9910484226603321 |
Abrardo Andrea
![]() |
||
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|