標題: Titlebook: Handbook of Digital Face Manipulation and Detection; From DeepFakes to Mo Christian Rathgeb,Ruben Tolosana,Christoph Busch Book‘‘‘‘‘‘‘‘ 202 [打印本頁] 作者: 建筑物的正面 時間: 2025-3-21 16:18
書目名稱Handbook of Digital Face Manipulation and Detection影響因子(影響力)
書目名稱Handbook of Digital Face Manipulation and Detection影響因子(影響力)學科排名
書目名稱Handbook of Digital Face Manipulation and Detection網(wǎng)絡公開度
書目名稱Handbook of Digital Face Manipulation and Detection網(wǎng)絡公開度學科排名
書目名稱Handbook of Digital Face Manipulation and Detection被引頻次
書目名稱Handbook of Digital Face Manipulation and Detection被引頻次學科排名
書目名稱Handbook of Digital Face Manipulation and Detection年度引用
書目名稱Handbook of Digital Face Manipulation and Detection年度引用學科排名
書目名稱Handbook of Digital Face Manipulation and Detection讀者反饋
書目名稱Handbook of Digital Face Manipulation and Detection讀者反饋學科排名
作者: Accrue 時間: 2025-3-21 21:11 作者: Anthem 時間: 2025-3-22 03:09
https://doi.org/10.1007/978-3-658-22513-1dentify inconsistencies with higher accuracy. In this chapter, we introduce several approaches to detect deepfakes. These approaches leverage different data modalities, including video and audio. We show that the presented methods achieve accurate detection for various large-scale datasets.作者: 多節(jié) 時間: 2025-3-22 07:45 作者: CHIP 時間: 2025-3-22 09:16 作者: Dissonance 時間: 2025-3-22 14:19 作者: Hemodialysis 時間: 2025-3-22 19:59
2191-6586 s.Includes the point of view of both academic institutions a.This open access book provides the first comprehensive collection of?studies?dealing with the hot topic of digital face manipulation?such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media 作者: 荒唐 時間: 2025-3-23 01:06
Regine Breusing,Solveig Steinmann-Lindner how dangerous deepfakes?are for both human and computer visions by showing how well these videos can fool face recognition?algorithms and na?ve human subjects. We also show how well the state-of-the-art deepfake detection?algorithms can detect deepfakes?and whether they can outperform humans.作者: 邪惡的你 時間: 2025-3-23 01:27
The Threat of?Deepfakes to?Computer and?Human Visions how dangerous deepfakes?are for both human and computer visions by showing how well these videos can fool face recognition?algorithms and na?ve human subjects. We also show how well the state-of-the-art deepfake detection?algorithms can detect deepfakes?and whether they can outperform humans.作者: Foam-Cells 時間: 2025-3-23 06:07 作者: Bph773 時間: 2025-3-23 10:26
https://doi.org/10.1007/978-3-663-12959-2lished by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.作者: companion 時間: 2025-3-23 16:30
Unterrichtenlernen und Forschenlerneninition and underlying challenges of the problem. Then, we present an overview of recent progress in talking face?generation. In addition, we introduce some widely used datasets and performance metrics. Finally, we discuss open questions, potential future directions, and ethical considerations in this task.作者: 成份 時間: 2025-3-23 21:26 作者: 能得到 時間: 2025-3-24 00:35 作者: ABYSS 時間: 2025-3-24 02:23 作者: 拔出 時間: 2025-3-24 08:39
Face Morphing Attack Detection Methodsformance. Different concepts of morphing attack detection?are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.作者: 善于騙人 時間: 2025-3-24 11:48
Handbook of Digital Face Manipulation and DetectionFrom DeepFakes to Mo作者: irreparable 時間: 2025-3-24 16:45
Digital Face Manipulation in?Biometric Systemsation, it is shown that different types of face manipulation, i.e.?retouching, face morphing, and swapping, can significantly affect the biometric?performance of face recognition systems?and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face 作者: 煩躁的女人 時間: 2025-3-24 22:14
Multimedia Forensics Before the Deep Learning Era some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review th作者: forthy 時間: 2025-3-25 00:37
Morph Creation and Vulnerability of Face Recognition Systems to Morphingss, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed作者: tangle 時間: 2025-3-25 06:19
Detection of AI-Generated Synthetic Facesentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face?generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in作者: growth-factor 時間: 2025-3-25 10:08 作者: creditor 時間: 2025-3-25 15:12 作者: 含沙射影 時間: 2025-3-25 17:41
Capsule-Forensics Networks for?Deepfake Detectionpter, we argue that our forensic-oriented capsule network?overcomes these limitations and is more suitable than conventional CNNs?to detect deepfakes. The superiority of our “Capsule-Forensics”network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic rout作者: Optometrist 時間: 2025-3-25 20:35
DeepFakes Detection: the? DeeperForensics ?Dataset and?Challengell number, of low quality, or overly artificial. Meanwhile, the large distribution gap between training data and actual test videos also leads to weak generalization ability. In this chapter, we present our on-going effort of constructing DeeperForensics-1.0, a large-scale forgery detection dataset,作者: 挖掘 時間: 2025-3-26 01:29 作者: 時代錯誤 時間: 2025-3-26 05:59
Book‘‘‘‘‘‘‘‘ 2022eadership is academic institutions and industry currently involved in?digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area..作者: 松緊帶 時間: 2025-3-26 10:48 作者: cardiovascular 時間: 2025-3-26 16:00
https://doi.org/10.1007/978-3-322-98878-2 some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review th作者: 反省 時間: 2025-3-26 17:04
Mediennutzung in Gesundheitsfachberufen,ss, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed作者: 純樸 時間: 2025-3-26 22:10 作者: cataract 時間: 2025-3-27 05:02 作者: 我吃花盤旋 時間: 2025-3-27 06:09 作者: Ambiguous 時間: 2025-3-27 13:09
Schreibdidaktik und Schreibunterrichtpter, we argue that our forensic-oriented capsule network?overcomes these limitations and is more suitable than conventional CNNs?to detect deepfakes. The superiority of our “Capsule-Forensics”network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic rout作者: nettle 時間: 2025-3-27 16:19 作者: cuticle 時間: 2025-3-27 19:06
übungen Zum Kapitel 2 - Visuelle Mediene a serious degradation in performance of the MAD methods. In addition, more advanced tools exist to manipulate the face morphs, like manual retouching?or morphing?artifacts can be concealed by printing and scanning a photograph (as used in the passport application process in many countries). Furthe作者: 暫停,間歇 時間: 2025-3-27 21:56 作者: 小樣他閑聊 時間: 2025-3-28 03:18
https://doi.org/10.1007/978-3-663-12959-2 the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: . entire face synthesis, . identity swap, . face morphing, . attribute manipulation, 作者: 抗原 時間: 2025-3-28 07:44
https://doi.org/10.1007/978-3-663-07726-8 the world. The processing of digitally manipulated face images within a face recognition system?may lead to false decisions and thus decrease the reliability of the decision system. This necessitates the development of manipulation detection?modules which can be seamlessly integrated into the proce作者: 使饑餓 時間: 2025-3-28 12:33
https://doi.org/10.1007/978-3-322-98878-2processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we作者: scrape 時間: 2025-3-28 18:24
Volker Nitzschke,Jürgen Langhammer-Jaeschkevelop and evaluate DeepFake detection?algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake?videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, ., which contains 5,?639作者: 可互換 時間: 2025-3-28 20:38
Regine Breusing,Solveig Steinmann-Lindnerts. The concern for the impact of the widespread deepfake?videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes?are for both human and computer visions by showing how well these videos can fool face recognition?algorithms and na?ve human作者: SLAG 時間: 2025-3-29 00:28
Mediennutzung in Gesundheitsfachberufen,systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face作者: CUR 時間: 2025-3-29 06:37
https://doi.org/10.1007/978-3-662-53963-7ep neural networks has opened up the possibility of scaling it to multiple applications. Despite the improvement in performance, deep network-based Face Recognition Systems (FRS)?are not well prepared against adversarial attacks?at the deployment level. The output performance of such FRS?can be dras作者: 拋射物 時間: 2025-3-29 09:18
Unterrichtenlernen und Forschenlernenm applications, such as teleconferencing, movie dubbing, and virtual assistant. The emergence of deep learning?and cross-modality research has led to many interesting works that address talking face?generation. Despite great research efforts in talking face generation, the problem remains challengin作者: 緯線 時間: 2025-3-29 11:31 作者: 揮舞 時間: 2025-3-29 17:41
Horst Schecker,Dietmar H?tteckechnically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of s作者: BOOR 時間: 2025-3-29 21:41
https://doi.org/10.1007/978-3-658-22513-1ble easy, credible manipulations of multimedia assets. Some even utilize advanced artificial intelligence?concepts to manipulate media, resulting in videos known as .. Social media platforms and their “echo chamber” effect propagate fabricated digital content at scale, sometimes with dire consequenc作者: 紳士 時間: 2025-3-30 03:25
,Lerneffektivit?t ausgew?hlter Methoden,rate?using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG?is useful for the detection of DeepFake?videos. We analyze the re作者: arboretum 時間: 2025-3-30 07:49 作者: 縮短 時間: 2025-3-30 09:52
L?sungsvorschl?ge für die übungsbeispieleopment of creative content creation. The emergence and easy accessibility of such techniques, however, also cause potential unprecedented ethical and moral issues. To this end, academia and industry proposed several effective forgery detection methods. Nonetheless, challenges could still exist. (1) 作者: anaphylaxis 時間: 2025-3-30 13:38 作者: patriot 時間: 2025-3-30 18:51 作者: white-matter 時間: 2025-3-30 22:34
An Introduction to?Digital Face Manipulation the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: . entire face synthesis, . identity swap, . face morphing, . attribute manipulation, 作者: chastise 時間: 2025-3-31 04:35 作者: Sciatica 時間: 2025-3-31 07:16 作者: 報復 時間: 2025-3-31 12:06
Toward the?Creation and?Obstruction of?DeepFakesvelop and evaluate DeepFake detection?algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake?videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, ., which contains 5,?639作者: BADGE 時間: 2025-3-31 17:16
The Threat of?Deepfakes to?Computer and?Human Visionsts. The concern for the impact of the widespread deepfake?videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes?are for both human and computer visions by showing how well these videos can fool face recognition?algorithms and na?ve human作者: Intervention 時間: 2025-3-31 18:54 作者: watertight, 時間: 2025-3-31 22:07
