作者: Ascribe 時間: 2025-3-21 22:25 作者: HARD 時間: 2025-3-22 00:35 作者: 外科醫(yī)生 時間: 2025-3-22 06:49
Good or Evil: Generative Adversarial Networks in Digital Forensics,forensic tools to fool classifiers. The adversary may either remove or modify crucial evidence present in data or create completely new data which closely reflects the original. In this work, we present a literature overview on the application of GANs in the digital forensics domain to demonstrate t作者: bourgeois 時間: 2025-3-22 10:30 作者: 停止償付 時間: 2025-3-22 13:28
Generative Adversarial Networks for Artificial Satellite Image Creation and Manipulation,ing..With an appropriately trained version of cycle GAN architecture, the objective of the land cover transfer is to effectuate a change in the land cover from modified images of vegetation to barren land and vice versa. By employing the pix2pix GAN architecture, the seasonal transfer technique achi作者: Fretful 時間: 2025-3-22 21:07
Domain Specific Information Based Learning for Facial Image Forensics,entify the suspects involved in crime, forensic experts follow manual comparison which is a time-consuming task. The main objective of this chapter is to discuss the importance of forensics and the various universities, laboratories, and applications established in this discipline. Domain specific m作者: NOVA 時間: 2025-3-22 21:13 作者: Brochure 時間: 2025-3-23 05:18 作者: Oscillate 時間: 2025-3-23 09:21
Using Vocoder Artifacts For Audio Deepfakes Detection,with the vocoder identification system. We employ a self-supervised representation learning (SSRL) approach, treating vocoder identification as a pretext task. Doing so ensures that the front-end feature extraction module is constrained and optimized to build the final binary classifier for syntheti作者: 出生 時間: 2025-3-23 10:39 作者: 恩惠 時間: 2025-3-23 17:31 作者: kyphoplasty 時間: 2025-3-23 18:33
E. Casas,J.-P. Raymond,H. Zidaniwe analyze the performance of Comprint when manipulated images are exposed to differences in JPEG compression, such as small quality factor differences, non-standard quantization tables, and DCT implementations, as well as when exposed to recompression. The results demonstrate that Comprint is robus作者: Expostulate 時間: 2025-3-24 01:01 作者: amphibian 時間: 2025-3-24 05:43 作者: 名義上 時間: 2025-3-24 07:01 作者: 協(xié)議 時間: 2025-3-24 11:51 作者: Abutment 時間: 2025-3-24 15:16 作者: savage 時間: 2025-3-24 21:39
https://doi.org/10.1007/978-3-540-71119-3es of attacks. Randomization of features was found to contribute to disrupting attack transferability, even in cases where attack transferability can be prevented by retraining the detector or simply varying the detector architecture.作者: Peculate 時間: 2025-3-25 00:56
https://doi.org/10.1007/978-3-540-71119-3with the vocoder identification system. We employ a self-supervised representation learning (SSRL) approach, treating vocoder identification as a pretext task. Doing so ensures that the front-end feature extraction module is constrained and optimized to build the final binary classifier for syntheti作者: Ingest 時間: 2025-3-25 04:45
Book 2024cape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addresses techniques and methodologies related to digital image forensics. It discusses advanced techniques for image manipulation detection, including the u作者: 慢跑鞋 時間: 2025-3-25 11:12
https://doi.org/10.1007/978-3-540-71119-3litudes to increase resilience, while being optimized to be indistinguishable from non-watermarked weights. Experiments show that the proposed approach is able to handle large payloads without significantly affecting the accuracy of the network, and is resistant to various types of network modifications and reuse.作者: FLACK 時間: 2025-3-25 15:36 作者: 進步 時間: 2025-3-25 19:29
1568-2633 chitectures that pose new serious security threats against e.This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in t作者: sinoatrial-node 時間: 2025-3-25 22:20
https://doi.org/10.1007/978-3-540-71119-3ms and anti forensic targets. We provide an overview of a number of popular anti-forensic measures that apply to media forensics, along with descriptions of every measure. We also qualitatively evaluate anti forensic measures on the conflict of interest between convenience and effectiveness of the measure.作者: euphoria 時間: 2025-3-26 03:26
Anti Forensic Measures and Their Impact on Forensic Investigations,ms and anti forensic targets. We provide an overview of a number of popular anti-forensic measures that apply to media forensics, along with descriptions of every measure. We also qualitatively evaluate anti forensic measures on the conflict of interest between convenience and effectiveness of the measure.作者: Confidential 時間: 2025-3-26 04:29 作者: notion 時間: 2025-3-26 10:30
E. Casas,J.-P. Raymond,H. Zidani”. Detecting image forgeries is important because image manipulation tools are prevalent and make it easy to spread misinformation. Existing forgery detection methods are still challenged to accurately detect image manipulations, especially when the doctored image is of a low quality (e.g., due to r作者: 憤世嫉俗者 時間: 2025-3-26 15:16
https://doi.org/10.1007/978-3-0348-8849-3ally, GANs can be used to improve the quality of images and videos, generate synthetic data such as fictional characters and deep fakes, and apply data adaptation. In digital forensics, GANs can be used to improve the quality of machine learning (ML) algorithms. This is because the existing ML-based作者: 悠然 時間: 2025-3-26 16:49
https://doi.org/10.1007/978-3-0348-8001-5hniques. These algorithms utilize Siamese neural networks to detect and localize spliced contents by identifying inconsistencies in an image’s forensic traces. At the same time, deep learning has also enabled the researchers to develop new types of anti-forensic attack that can fool forensic algorit作者: 火花 時間: 2025-3-26 22:34
H. T. Banks,S. C. Beeler,H. T. Trandeep learning architectures (DL),Synthetic satellite images offer a wide range of potential applications, including the generation of massive, labeled datasets for artificial intelligence (AI) applications, manipulated image detection, and natural disaster monitoring and detection. Although deep lea作者: 血友病 時間: 2025-3-27 04:25 作者: tenuous 時間: 2025-3-27 07:41
M. Bergounioux,T. M?nnikk?,D. Tiba counter technology to linguistic steganography, aims at revealing the existence of additional data within unknown texts. Early linguistic steganography algorithms alter a text carrier to embed additional data, which limits the embedding payload due to the extremely small number of changeable operat作者: surrogate 時間: 2025-3-27 13:31
https://doi.org/10.1007/978-3-540-71119-3addition to the improvement in the robustness of forensic detectors to targeted attacks mentioned in Chen et al. (IEEE Trans Inf Forensics Secur 14(9):2454–2469, 2019). This paper specifically investigates the transferability of adversarial cases targeting the original Convolutional Neural Network (作者: BUCK 時間: 2025-3-27 14:05 作者: 證明無罪 時間: 2025-3-27 21:06 作者: atrophy 時間: 2025-3-28 01:09
https://doi.org/10.1007/978-3-540-71119-3 become crucial to combat these challenges. In this work, we propose a novel approach for detecting synthetic human voices by leveraging the identification of artifacts generated by neural vocoders in audio signals. Neural vocoders are specialized neural networks synthesizing waveforms using tempora作者: 有雜色 時間: 2025-3-28 02:10
Adversarial Multimedia Forensics978-3-031-49803-9Series ISSN 1568-2633 Series E-ISSN 2512-2193 作者: otic-capsule 時間: 2025-3-28 08:54 作者: 中國紀念碑 時間: 2025-3-28 12:11 作者: llibretto 時間: 2025-3-28 15:15
https://doi.org/10.1007/978-3-031-49803-9Adversarial Attacks; DeepFake Detection; Multimedia Forensics; Digital Forensics; Image Forensics; Video 作者: dura-mater 時間: 2025-3-28 22:23 作者: CRUMB 時間: 2025-3-29 00:36
Model Poisoning Attack Against Federated Learning with Adaptive Aggregation, a new era of collaborative data-driven insights. However, the growing adoption of FL brings forth the need to scrutinize its vulnerabilities and security challenges, particularly concerning adversarial attacks. This book chapter delves into the intricate realm of FL’s susceptibility to adversarial 作者: 正面 時間: 2025-3-29 06:38 作者: Corral 時間: 2025-3-29 08:12 作者: ORBIT 時間: 2025-3-29 14:45
Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms,hniques. These algorithms utilize Siamese neural networks to detect and localize spliced contents by identifying inconsistencies in an image’s forensic traces. At the same time, deep learning has also enabled the researchers to develop new types of anti-forensic attack that can fool forensic algorit作者: 法律的瑕疵 時間: 2025-3-29 19:37
Generative Adversarial Networks for Artificial Satellite Image Creation and Manipulation,deep learning architectures (DL),Synthetic satellite images offer a wide range of potential applications, including the generation of massive, labeled datasets for artificial intelligence (AI) applications, manipulated image detection, and natural disaster monitoring and detection. Although deep lea作者: Lament 時間: 2025-3-29 23:39
Domain Specific Information Based Learning for Facial Image Forensics,cs is one of the sub fields of multimedia forensics which involves any kind of digital images such as face, fingerprint etc. Security systems are one of the most sophisticated systems to protect assets and privacy in recent years. The demand for fast, accurate identification and authentication is in作者: 和諧 時間: 2025-3-30 01:07
