I meme si stanno trasformando in deepfake

Karen Hao

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I meme si stanno trasformando in deepfake

机译：模因被转变为浅锅

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Grace Windheim aveva già sentito parlare di deepfake, ma non aveva mai pensato a come crearne uno. È statoun meme virale che utilizzava la tecnologia per produrre deepfake che l'ha portata a ricercare la possibilità escoprire che era super facile e completamente gratuito. Nel giro di un giorno, aveva pubblicato un tutorial suYouTube per spiegare agli altri cosa fare. "Creare un deepfake e sovrapporre l'audio non è così complicatocome si pensa", dice nel video, pubblicato il 4 agosto. Da allora ha raccolto 360.000 visualizzazioni.

机译：格雷德海姆已经听说过DeepFake，但他从未想过如何创造一个。已经一种使用技术生产DeepFake的病毒模型，导致您寻找可能性e 了解它是超级容易和完全免费的。在一天之内，他发表了一个教程 YouTube向别人解释该做什么。 “创造一个深脸和叠加音频并不是那么复杂正如您所想，“他在视频中在8月4日发布。从那时起，他会收集36万次观看。

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《Technology Review》 |2020年第8期|1-3|共3页
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Karen Hao;
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1. Political memes and the politics of memes: A methodological proposal for content analysis of online political memes [J] . Viktor Chagas, Fernanda Freire, Daniel Rios, First Monday . 2019,第2期

机译：政治模因与模因政治：在线政治模因内容分析的方法学建议
2. L'industria si sta trasformando [J] . Edoardo Oldrati Tecnologie Meccaniche . 2019,第6期

机译：该行业正在变革
3. Memes: A motif analysis environment in R using tools from the MEME Suite [J] . Spencer L.Nystrom, Daniel J.McKay PLoS Computational Biology . 2021,第9期

机译：MEMES：使用MEME套件的工具中的r主题分析环境
4. Awareness to Deepfake: A resistance mechanism to Deepfake [C] . Mohammad Faisal Bin Ahmed, M. Saef Ullah Miah, Abhijit Bhowmik, International Congress of Advanced Technology and Engineering . 2021

机译：对DeepFake的意识：DeepFake的抵抗机制
5. DeepFakes Detection in Videos Using Feature Engineering Techniques in Deep Learning Convolution Neural Network Frameworks [D] . Burroughs, Sonya. 2021

机译：使用深度学习卷积神经网络框架的特征工程技术在视频中检测视频
6. What Do You Meme Professor? An Experiment Using Memes in Pharmacy Education [O] . Joshua D. Brown 2020

机译：你是什么Meme教授？在药房教育中使用MEMES的实验
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。

I meme si stanno trasformando in deepfake

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