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譯科技 | 什么?!有人模仿你的臉,還有人模仿你全身?

You’ve been warned: Full body deepfakes are the next step in AI-based human mimicry

數據觀丨王婕(譯)

  This developing branch of synthetic media technology has commercial applications—but also has the potential to disrupt elections and spread disinformation.

  以下這項發展中的合成媒體技術分支具備商業用途,但也有可能被用來擾亂選舉和散布謠言。

  In Russian novelist Victor Pelevin’s cyberpunk novel, Homo Zapiens, a poet named Babylen Tatarsky is recruited by an old college buddy to be an advertising copywriter in Moscow amid post-Soviet Russia’s economic collapse. With a talent for clever wordplay, Tatarsky quickly climbs the corporate ladder, where he discovers that politicians like then-Russian president Boris Yeltsin and major political events are, in fact, virtual simulations. With the advent of ever-more sophisticated deepfakes, it feels as if something like Pelevin’s vision is slowly coming true.

  俄羅斯小說家維克多·佩列文(Victor Pelevin)的賽博朋克小說《Homo Zapiens》中,一位名叫Babylen Tatarsky的詩人在蘇聯解體,俄羅斯經濟面臨崩潰之際,被一位在莫斯科的大學老友聘為廣告文案撰稿人。Tatarsky憑借著巧妙的文字天賦一路水漲船高,而他也逐漸發現了這樣一個事實:時任俄羅斯總統的葉利欽等政要和當時的重大政治事件,實際上都是虛擬仿真的產物。放眼現在,隨著日益純熟的“深度換臉”技術出現,佩列文的想象似乎正在慢慢變為現實。

  (數據觀注釋:【賽博朋克小說】賽博朋克小說屬于科幻小說的類型,興起于上世紀七十年代的美國,這一類故事里有大量對新興信息技術和生物科技的描寫,常常涉及跨國財團壟斷高新技術,故事的主角一般會設定成游走在社會主流之外的邊緣人,他們活在未來社會的陰暗面,喜歡修改電腦的軟硬件配置,崇尚改造身體,拒絕融入主流體制,靠著合法或者非法的技術手段鋌而走險,有時不惜與超級大公司對抗。這種高與低并存產生的反差,造成了一種特殊的美學效果,被概括為“高科技、低生活”六個字。)

  Within the field of deepfakes, or “synthetic media” as researchers call it, much of the attention has been focused on fake faces potentially wreaking havoc on political reality, as well as other deep learning algorithms that can, for instance, mimic a person’s writing style and voice. But yet another branch of synthetic media technology is fast evolving: full body deepfakes.

  在“深度換臉”(亦或被研究人員稱之為“合成媒體”)的領域內,眾人的注意力主要集中在可能對政治現實造成嚴重破壞的“虛假面孔”上,以及那些刻意模仿人寫作風格和聲音的深度學習算法。然而,如今合成媒體技術的另一個分支——“深度換身”正在迅速發展。

  In August 2018, University of California Berkeley researchers released a paper and video titled “Everybody Dance Now,” demonstrating how deep learning algorithms can transfer a professional dancers’ moves onto the bodies of amateurs. While primitive, it showed that machine learning researchers are tackling the more difficult task of creating full body deepfakes. Also in 2018, a team of researchers led by Dr. Bj?rn Ommer of Heidelberg University in Germany published a paper on teaching machines to realistically render human movements. And in April of this year, the Japanese artificial intelligence company Data Grid developed an AI that can automatically generate whole body models of nonexistent persons, identifying practical applications in the fashion and apparel industries.

  2018年8月,加州大學伯克利分校的研究人員發表了一篇題為《人人都在跳舞》的論文和視頻,展示了深度學習算法如何將專業舞者的動作轉移到業余舞者身上。雖然這一研究成果還有待完善,但已表明機器學習的研究人員正在著手更具挑戰的任務——“深度換身”。同年,德國海德堡大學的比約恩·奧默博士領導的一個研究團隊發表了一篇關于教會機器逼真還原人類動作的論文。今年4月,日本人工智能公司Data Grid開發了一種人工智能技術,可以自動生成不存在的人體全身模型,并證實了它在時尚和服裝領域中的實際應用。

  While it’s clear that full body deepfakes have interesting commercial applications, like deepfake dancing apps or in fields like athletics and biomedical research, malicious use cases are an increasing concern amid today’s polarized political climate riven by disinformation and fake news. For now, full body deepfakes aren’t capable of completely fooling the eye, but like any deep learning technology, advances will be made. It’s only a question of how soon full body deepfakes will become indistinguishable from the real.

  顯然,“深度換身”的確可以打造部分有趣的商業應用,比如換臉舞蹈應用程序,或者被應用在體育和生物醫學研究上,但惡意應用的案例在如今充斥著謠言和假新聞的政治背景下也愈發受到關注。雖然眼下“深度換身”還不能完全掩人耳目,但就像任何深度學習技術一樣,它也終將進步,“深度換身”想要魚目混珠,只是時間問題。

  SYNTHESIZING ENTIRE HUMAN BODIES人體合成

  To create deepfakes, computer scientists use Generative Adversarial Networks, or GANs. Comprised of two neural networks—a synthesizer or generative network, and a detector or discriminative network—these neural networks work in a feedback loop of refinement to create realistic synthetic images and video. The synthesizer creates an image from a database, while the latter, working from another database, determines whether the synthesizer’s image is accurate and believable.

  為了實現深度換臉,計算機科學家使用了生成式對抗網絡(GAN),它由兩個神經網絡組成:合成器/生成網絡,以及檢測器/鑒別網絡。這些神經網絡在精細的反饋回路中運行,生成真實的合成圖像和視頻。合成器從數據庫創建圖像,而檢測器則在另一個數據庫工作,用以確定合成器制造的圖像是否準確可信。

  The first malicious use of deepfakes appeared on a Reddit, where faces of actresses like Scarlett Johansson were mapped onto porn actors. Rachel Thomas of Fast.AI says that 95% of the deepfakes in existence are pornographic material meant to harass certain individuals with fake sexual acts. “Some of these deepfakes videos aren’t necessarily using very sophisticated techniques,” says Thomas. But, that is starting to change.

  “深度換臉”的首次惡意應用出現在Reddit(一個社交新聞站點)上,當時斯嘉麗·約翰遜等女演員的臉被移植到了色情電影演員的臉上。Fast.AI公司聯合創始人瑞秋·托馬斯表示,在目前已存在的“深度換臉”成品中,95%都是想通過“虛假”的不雅素材來進行個人騷擾。托馬斯說:“其中一些深度換臉視頻并不一定就使用了非常精細復雜的技術。”然而,這種情況正開始轉變。

  Farid points to the Chinese deepfake app Zao as being illustrative of how quickly the technology has evolved in less than than two years.

  法里德(新罕布什爾州漢諾威達特茅斯學院計算機科學教授)指出,中國的“深度換臉”應用程序“Zao”就很好地說明了這項技術在不到兩年的時間里發展得有多快。

  “The ones that I saw [from Zao] looked really, really good, and got around a lot of the artifacts, like in the movie versions where the face flickered,” says Farid. “It’s improving. Getting this as an app working at scale, downloading to millions of people, is hard. It’s a sign of the maturity of the deepfake technology.”

  “那些來自Zao的換臉視頻看起來真的非常棒,而且這些人造品有很多就跟在電影版本當中呈現的畫面一樣。”法里德認為,“這無疑是一個進步,要知道想讓這款app大規模應用且有數百萬人的下載量并不容易。這是‘深度換臉’技術走向成熟的標志。”

  “With deepfake images and videos, we’ve essentially democratized CGI technology,” he says. “We’ve taken it out of the hands of Hollywood studios and put it in the hands of YouTube video creators.”

  “通過深度換臉的圖像和視頻,我們基本上實現了CGI技術(通用網關接口,是一種重要的互聯網技術,可以讓一個客戶端,從網頁瀏覽器向執行在網絡服務器上的程序請求數據。CGI描述了服務器和請求處理程序之間傳輸數據的一種標準)的大眾化。”他進一步表示,“我們把CGI技術從好萊塢的電影公司中帶出來,交到了YouTube視頻制作者的手中。”

  Bj?rn Ommer, professor for computer vision at the Heidelberg University Collaboratory for Image Processing (HCI) & Interdisciplinary Center for Scientific Computing (IWR), leads a team that is researching and developing full body synthetic media. Like most researchers in the field, the group’s overall goal is to understand images and to teach machines how to understand images and video. Ultimately, he hopes the team gains a better understanding of how human beings understand images.

  海德堡大學圖像處理(HCI)和跨學科科學計算中心(IWR)計算機視覺教授比約恩?奧默領導了一個研究和開發人體合成媒體的團隊。與該領域的大多數研究人員一樣,該小組的總體目標是理解圖像,并教會機器如何認知圖像和視頻。最終,他希望團隊能夠更好地了解人類是如何理解圖像的。

  “We’ve seen synthetic avatars that have been created not just in the gaming industry but a lot of other fields that are creating revenue,” says Ommer. “For my group, in particular, it’s entirely different fields that we are considering, like biomedical research. We want to get a more detailed understanding of human or even animal posture over time, relating to disabilities and the like.”

  “我們已經看到了人體合成的化身不僅為游戲行業,還有許多其他領域都創造了營收,”奧默表示,“尤其是對我的團隊來說,我們考慮的是完全不同的領域,比如生物醫學研究。我們希望更詳細地了解人類甚至動物隨著時間的推移,在殘疾等類似情況下,身體姿態的演進。”

  There are critical differences between the processes of synthesizing faces and entire bodies. Ommer says that more research into face synthesis has been carried out. And there are a few reasons for this. First, any digital camera or smartphone has built-in face detection, technology that can be used for tasks like smile detection or to identify the person a viewer is looking at. Such applications can generate revenue, leading to more research. But they have also led to, as Ommer says, “a lot of data set assembly, data curation, and obtaining face images—the substrate upon which deep learning research is built.”

  人臉合成與人體合成的過程有著巨大的差異。奧默表示,當前人們已經對人臉合成進行了更多的研究,這其中有幾個原因。首先,任何數碼相機或智能手機都有內置的面部檢測技術,這種技術可以用于檢測像微笑這樣的任務,也可以用來識別觀眾的目視對象。這樣的應用程序能夠在產生營收的同時帶動更多研究。但正如奧默所說,它們也導致了“大量的數據集合、數據整理和人臉圖像獲取,而這些都是建立深度學習研究的基礎。”

  Secondly, and more interesting to Ommer, is that while each human face looks different, there isn’t much variability when the face is compared to an entire human body. “That is why the research on faces has come to a stage where I would say it is creating really decent results compared to entire human bodies with much more variability being there, much more complicated to handle, and much more to learn if you head in that direction,” says Ommer.

  其次,對奧默來說更有趣的是,雖然每個人的臉看起來都不一樣,但當把臉和整個身體放在一起相比時,變化其實并不大。“這就是為什么我說面部研究已經到了一定階段,與整個人體相比,它創造了非常好的結果,因為人體的可變性要大得多,處理起來更加復雜,如果你朝著這個方向前進,還需要學習更多”,奧默說。

  Ommer isn’t sure when full synthesized bodies will be of the quality that he and researchers want. Looking at the maturation of malicious deepfakes, however, Ommer notes that humans can already be tricked quite easily without fakes created by deep learning computer vision intelligence, artificial intelligence, or other technologies.

  奧默也不知道人體合成何時才能夠達到他和研究人員想要的標準。然而,縱觀那些不懷好意的深度換臉日益成熟,奧默指出,如果沒有通過深度學習計算機視覺智能、人工智能或其他技術制造的偽造品來一窺究竟,人類可能早就上當了。

  “But, if you want to make it appealing to larger society, it will take a few more years,” says Ommer, who says full body and other deepfakes will become cheaper and more prevalent. “The research community itself has moved in a direction—and this is very much appreciated by much of the community that is responsible for a lot of this steady progress that we see—where the algorithms are easily available, like on Github and so on. So, you can just download the most recent code from some paper, and then, without much knowledge of what’s under the hood, just apply it.”

  “但是,如果你想讓它在更大的社會層面上被接受,那還需要幾年的時間,”奧默說,“深度換身”和其他深度造假將變得更加低廉和更普遍。“研究界本身已經朝著一個好的方向發展,這一點得到了許多研究團體的高度贊賞,且這些團體對我們能夠更加方便地獲取算法這一進程的穩定發展發揮了很大的作用,比如github等。所以,你可以從一些論文上下載最新的代碼,然后在不太了解隱藏內容的情況下,直接應用它。”

  FEELING “POWERLESS AND PARALYZED”感到“力不從心”

  Not every person will be able to create a “blockbuster deepfake.” But, given more time, Ommer says money will no longer be an issue in terms of computational resources, and the applicability of software will also become much easier. Farid says that with full body deepfakes, malicious creators will be able to work deepfake technology’s typically stationary figure talking directly into the camera, making targets do and say things they never would.

  不是每個人都能創造出“轟動一時”的深度換臉。然而,奧默認為,隨著時間的推移,金錢將不再成為獲取計算資源方面的阻礙,軟件的適用性也將變得容易得多。法里德說,有了“深度換身”,不懷好意的人就可以利用深度換臉技術中的典型靜止圖像直接在錄像中開口說話,讓“目標對象”為所欲為。

  Tom Van de Weghe, an investigative journalist and foreign correspondent for VRT (the Flemish Broadcasting Corporation), worries that journalists, but also human rights activists and dissidents, could have footage of them weaponized by full body deepfakes.

  VRT電臺(佛蘭德廣播公司)的調查記者兼駐外記者湯姆范德韋赫擔心,記者、還有人權活動人士和持不同政見者們,都有可能被“深度換身”武器化。

Tom Van de Weghe [Photo: courtesy of Tom Van de Weghe]

湯姆范德韋赫(圖片由本人提供)

  The explosion of fake news during the 2016 election, and the rise of deepfakes in 2017 inspired Van de Weghe to research synthetic media. In the summer of 2018, he began a research fellowship at Stanford University to study ways of battling the malicious use of deepfakes.

  2016年大選期間假新聞的激增,以及2017年“深度換臉”的興起,激發了范德韋赫對合成媒體的研究。2018年夏天,他在斯坦福大學開始了一項旨在對抗惡意使用“深度換臉”的方法研究。

  “It’s not the big shots, the big politicians, and the big famous guys who are the most threatened,” says Van de Weghe. “It’s the normal people—people like you, me, female journalists, and sort of marginalized groups that could become or are already becoming the victims of deepfakes.”

  “受威脅最大的不是大人物、政客和名人,”范德韋赫表示,“只有普通人——像你、我、女記者,以及那些可能成為或已經成為深度換臉受害者的邊緣群體。”

  Two weeks ago, Dutch news anchor Dionne Stax discovered her face “deepfaked” onto a porn actress’s body, after the video was uploaded to PornHub and distributed on the internet. Although PornHub quickly removed the video, Van de Weghe says that the damage to her reputation had already been done. He also points to China’s AI public broadcasters as proof that the Chinese government has the capability to pull off realistic deepfakes.

  兩周前,荷蘭新聞主播迪翁·斯塔克斯發現自己的臉被“深度換臉”技術映射到了一名色情女演員的身上,該視頻還被上傳到PornHub網站(全球最大的色情視頻分享類網站之一)并在互聯網上廣泛傳播。盡管PornHub很快就刪除了這段視頻,但范德韋赫表示,她的聲譽已經受到了損害。

  To imagine how a full body deepfake might work, Van de Weghe points to 2018 footage of Jim Acosta, CNN’s chief White House correspondent. In a video clip uploaded by Paul Joseph Watson, an editor at conspiracy theory site Infowars, Acosta seems to aggressively push a white house staffer trying to take his microphone. The original clip, broadcast by C-SPAN, differs markedly from Watson’s. The Infowars editor claimed he didn’t doctor the footage and attributed any differences to “video compression” artifacts. But, as The Independent demonstrated in a side-by-side analysis of the videos in an editing timeline, Watson’s video is missing several frames from the original. A full body deepfake could, like editing video frames, alter the reality of an event.

  為了更好地想象“深度換身”是如何工作的,范德韋赫提到了2018年CNN首席白宮記者吉姆·阿科斯塔的鏡頭。在陰謀論網站Infowars,編輯保羅約瑟夫沃森上傳了一段視頻:阿科斯塔似乎咄咄逼人地推著一名試圖拿他麥克風的白宮工作人員。這與C-SPAN(美國一家提供公眾服務的非營利性的媒體公司)播出的原始片段有明顯不同。Infowars的編輯聲稱他并沒有篡改視頻,并將所有差異都歸因于“視頻壓縮”。但是,正如《獨立報》對視頻進行的時間軸編輯分析顯示,沃森的視頻的確缺少了原視頻的其中幾幀。“深度換身”就像編輯視頻時對幀數進行改動一樣,可以改變事件的真實性。

  Deeptrace Labs, founded in 2018, is a cybersecurity company that is building tools based on computer vision and deep learning to analyze and understand videos, particularly those that could be manipulated or synthesized by any sort of AI. Company founder Giorgio Patrini, previously a postdoc researcher on deep learning at the DELTA Lab, University of Amsterdam, says that a few years ago he started investigating how technology could prevent or defend against future misuse of synthetic media.

  成立于2018年的Deeptrace Labs是一家網絡安全公司,正在開發基于計算機視覺和深度學習的工具,以分析和理解視頻,尤其是那些可以被任何人工智能操縱或合成的視頻。該公司創始人喬治?帕特里尼曾在阿姆斯特丹大學德爾塔實驗室從事深度學習的博士后研究。他表示,幾年前自己開始研究技術如何預防或防范未來合成媒體的濫用。

  Patrini believes that malicious deepfakes, made up of a combination of synthetic full bodies, faces, and audio, will soon be used to target journalists and politicians. He pointed to a deepfake porn video that featured Indian journalist Rana Ayyub’s face swapped onto a porn actress’s body, part of a disinformation campaign intended to descredit her investigative reporting after she publicly pushed for justice in the rape and murder of an 8-year old Kashmiri girl. And in March, Deeptrace Labs looked into a purported deepfake of Gabon President Ali Bongo, who had recently suffered a stroke. While many in the African country thought Bongo’s immobile face, eyes, and body suggested a deepfake—including the Gabon military, which launched an unsuccessful coup based on this belief—Patrini told Mother Jones that he did not believe the video of the president had been synthesized.

  帕特里尼認為,由人體合成、人臉合成和音頻合成組成的惡意深度造假,將很快被用來攻擊記者和政客。他提到了一段深度換臉的色情視頻,視頻中印度記者拉娜·阿尤布的臉被換成了一名色情女演員的身體,作為這場虛假信息運動的一部分,這一行為的目的就在于抹黑她的調查報道。此前,她公開要求對強奸和謀殺一名8歲克什米爾女孩的行為進行司法審判。今年3月,Deeptrace Labs對加蓬總統阿里·邦戈的“深度換臉視頻”進行了調查。盡管這個非洲國家的許多人,包括加蓬軍隊在內都認為邦戈一動不動的臉、眼睛和身體暗藏著一個深度騙局,并基于此發動了一場不成功的政變,帕特里尼仍向《瓊斯母親》雜志表示,他不相信總統的視頻是合成的。

  “We couldn’t find any reasons to believe it was a deepfake, and I think that was later confirmed that the president is still alive but that he’d had a stroke,” says Patrini. “The main point I want to make here is that it doesn’t matter if a video is a deepfake or not yet—it’s that people know that it can spark doubt in public opinion and potentially violence in some places.”

  “我們找不到任何理由相信這是深度換臉的結果。我認為總統還活著,這一猜想隨后也被證實,不過他實際上是中風了。”帕特里尼說:“我想在這里指出的重點是,問題不在于視頻到底是真是假,重要的是人們很清楚它會在公眾輿論中引發懷疑,在某些地方還可能引發暴力。”

[Photo: courtesy of Deep trace Labs]

(圖片由Deeptrace Labs提供)

  Recently, Van de Weghe learned that a political party operative approached one of the most popular deepfake creators, requesting a deepfake to damage a certain individual. Such custom, made-to-order deepfakes could become big business.

  最近,范德韋赫了解到,一名政黨人士正在接觸“深度換臉”最受歡迎的創造者之一,并要求其運用這項技術來中傷某人。這種定制的“深度換臉”可能會成為一門大生意。

  “There is money to be earned with deepfakes,” says Van de Weghe. “People will order it. So, a government doesn’t have to create a deepfake—they just have to contact a person who is specialized in deepfakes to create one.”

  “‘深度換臉’會成為人們謀利的工具,”范德韋赫說,“人們會為它買單。所以,政府并不需要親自上陣,他們只需要聯系一個專門干這行的人就可以了。”

  The Wall Street Journal recently reported that a UK energy company CEO was fooled into transferring $243,000 to the account of a Hungarian supplier. The executive said he believed he was talking to his boss, who had seemingly approved the transaction. Now, the CEO believes he was the victim of an audio deepfake scam known as vishing. Farid believes other fraudulent deepfake financial schemes, which might include full body deepfakes, are only a matter of time.

  《華爾街日報》最近的報道稱,一家英國能源公司的首席執行官被騙,將24.3萬美元轉入了一家匈牙利供應商的賬戶。這位高管說,他相信自己是在和老板談話,而且他的老板似乎也已經批準了這筆交易。現在,這位首席執行官已經意識到他遭遇了一種名為“網絡釣魚”的深度換音造假。法里德認為,深度造假技術,甚至包括“深度換身”技術,在金融領域的欺詐很有可能呈肆虐之勢。

  “I could create a deepfake video of Jeff Bezos where he says that Amazon stock is going down,” says Farid. “Think of all of the money that could be made shorting Amazon stock. By the time you rein it in, the damage has already been done. . . . Now imagine a video of a Democratic party nominee saying illegal or insensitive things. You don’t think you can swing the vote of hundreds of thousands of voters the night before an election?”

  “我可以制作一個杰夫·貝佐斯的深度換臉視頻,讓他在里面說亞馬遜的股票正在下跌。”法里德說,“想想看,做空亞馬遜股票能賺多少錢。當你控制它的時候,傷害已經造成了……現在再想象一下,當你看到一個民主黨候選人說一些非法或漠不關心的話的視頻時,你還認為你不能在選舉前一天晚上左右成千上萬選民的投票嗎?”

  Farid thinks a combination of social media and deepfake videos, whether of faces or full bodies, could easily wreak havoc. Social media companies are largely unable or unwilling to moderate their platforms and content, so deepfakes can spread like wildfire.

  法里德認為,社交媒體和深度造假視頻的結合,無論是“深度換臉”還是“深度換身”,都很容易產生極大的不良影響。社交媒體公司經常無力或不愿調整他們的平臺和內容,因此深度換臉可以像野火一樣蔓延。

  “When you pair the ability to create deepfake content with the ability to distribute and consume it globally, it’s problematic,” he says. “We live in a highly polarized society, for a number of reasons, and people are going to think the worst of the people they disagree with.”

  “當你把深度換臉的能力與在全球散布和消費這些內容的能力結合起來時,麻煩就來了。”他表示,“出于很多原因,我們生活在一個高度分化的社會,也因此人們常常會把意見相左的人往壞處想。”

  But for Fast.AI’s Thomas, deepfakes are almost unnecessary in the new cyber skirmishes to negatively influence the political process, as governments and industry already struggle with fake information in the written form. She says the risks aren’t just about technology but human factors. Society is polarized, and vast swaths of the United States (and other countries) no longer have shared sources of truth that they can trust.

  但對于Fast.AI公司聯合創始人瑞秋·托馬斯來說,在新的網絡沖突中,深度換臉對政治進程產生的負面影響幾乎可以忽略不計,因為政府和行業已經在與書面形式的虛假信息作斗爭。她說,這些風險不僅與技術有關,還與人為因素有關。社會兩極分化背景下,美國和其他國家的大片地區不再有可以完全信任的事實來源。

  This mistrust can play into the hands of politically motivated deepfake creators. When a deepfake is debunked, as privacy scholar Danielle Citron noted, it can suggest to those who bought the lie that there is some truth to it. Citron calls this “the liar’s dividend.” Farid thinks advancements in full body deepfake technology will make the overall problem of this type of nefarious deepfakery worse. The technology is evolving fast, spurred by university research like “Everybody Dance Now” and private sector initiatives such as Zao to monetize deepfakes.

  這種不信任可能會讓有政治動機的“深度換臉”制造者有機可乘。正如隱私學者丹妮爾?西特龍所指出的,當深度換臉被揭穿時,它可以向那些相信謊言的人暗示,謊言是有一定道理的。西特恩稱這是“說謊者的紅利”。法里德認為,“深度換身”技術的進步將在整體上使這類惡意深度造假的問題變得更糟。這項技術如今正在快速發展,很有可能在諸如《人人都在跳舞》等高校研究和“Zao”APP開發商的教唆下,將“深度換臉”合法化。

  “Once you can do full body, it’s not just talking heads anymore: you can simulate people having sex or killing someone,” Farid says. “Is it just around the corner? Probably not. But eventually it’s not unreasonable that in a year or two that people will be able to do full body deepfakes, and it will be incredibly powerful.”

  “一旦能對全身動作進行合成模仿,那時畫面上就不再只是出現一個講話的腦袋了,你甚至可以假裝成別人做不雅事或殺人。”法里德說:“是不是已經可以這樣操作了?目前可能還不能實現。但一兩年后,人們就能做到全身深度模仿,這一猜想并不是沒有道理的,而且一旦實現會發揮非常強大的作用。”

  INDUSTRY RESPONSE行業回應

  Currently, no consensus approach to rooting out deepfakes exists within the tech industry. A number of different techniques are being researched and tested.

  目前,科技行業還沒有達成根除深度換臉的共識,許多不同的技術正在研究和測試中。

  Van de Weghe’s research team, for instance, created a variety of internal challenges that explored different approaches. One team investigated digital watermarking of footage to identify deepfakes. Another team used blockchain technology to establish trust, which is one of its strengths. And yet another team identified deepfakes by using the very same deep learning techniques that created them in the first place.

  例如,范德韋赫的研究團隊創造了各種內部挑戰,并探索了不同的方法。其中一個研究小組研究了膠片的數字水印以識別深度換臉。另一個團隊則試圖使用區塊鏈技術來建立信任,這也是區塊鏈技術本身的優勢之一。另外,還有一個團隊通過使用與最初“深度換臉”相同的深度學習技術來識別贗品。

  “Some Stanford dropouts created Sherlock AI, an automatic deepfake detection tool,” says Van de Weghe. “So, they sampled some convolutional models and then they look for anomalies in a video. It’s a procedure being used by other deepfake detectors, like Deeptrace Labs. They use the data sets called FaceForensics++, and then they test it. They’ve got like 97% accuracy and work well with faces.”

  “Sherlock AI是一個自動檢測深度換臉的工具,由來自斯坦福大學的輟學生開發,”范德韋赫介紹,“因此,他們取樣了一些卷積模型,然后在視頻中尋找異常。這一過程也被其他深度換臉檢測器使用,比如Deeptrace Labs。他們使用名為FaceForensics++的數據集,然后對其進行測試,其準確率高達97%,對人臉的識別效果也很好。”

  Deeptrace Labs’ API-based monitoring system can see the creation, upload, and sharing of deepfake videos. Since being founded in 2018, the company has found over 14,000 fake videos on the internet. Insights gleaned by Deeptrace Labs’ system can inform the company and its clients about what deepfake creators are making, where the fakes came from, what algorithms they are using, and how accessible these tools are. Patrini says his team found that 95% of deepfakes are face swaps in the fake porn category, with most of them being a narrow subset of celebrities. So far, Deeptrace Labs hasn’t seen any full body synthesis technology being used out in the wild.

  Deeptrace Lab基于API(應用程序接口)的監控系統可以監測到深度偽造視頻的創建、上傳和共享。自2018年成立以來,該公司已經在互聯網上發現了超過1.4萬個虛假視頻。Deeptrace Lab的系統收集到的信息可以告訴公司及其客戶,深度偽造品的制作者在做什么,偽造品來自哪里,他們在使用什么算法,以及這些工具的可訪問性如何。帕特里尼說,他的團隊發現,95%的深度偽造品都是虛假色情類的“深度換臉”產品,大多數視頻來自于一小撮名人。到目前為止,Deeptrace Lab還沒有看到任何在野外應用的全身合成技術產品。

  “You cannot really summarize a solution for these problems in a single algorithm or idea,” says Patrini. “It’s about building several tools that can tell you different things about synthetic media overall.”

  “你不能用單一的算法或想法來總結這些問題的解決方案,”帕特里尼表示,“這與建立一些能告訴你合成媒體不同情況的工具有關。”

  Van de Weghe thinks the next big thing in anti-deepfake technology will be soft biometric signatures. Every person has their own unique facial tics—raised brows, lip movements, hand movements—that function as personal signatures of sorts. Shruti Agarwal, a researcher at UC-Berkeley, used soft biometric models to determine if such facial tics have been artificially created for videos. (Agarwal’s thesis adviser is fake video expert and Dartmouth professor Hany Farid.)

  范德韋赫認為反深度換臉技術的下一個重大發明將是軟生物特征識別技術。每個人都有自己獨特的面部表情——揚起的眉毛、嘴唇的動作、手部的動作,這些都可以作為某種個人特征。加州大學伯克利分校的研究人員施盧蒂·阿加瓦爾使用了軟生物計量模型來確定一些畫面里的面部抽搐是否是為了視頻效果而人為的結果。(阿加瓦爾的論文導師是深度造假視頻專家、達特茅斯大學教授哈尼·法里德。)

  “The basic idea is we can build these soft biometric models of various world leaders, such as 2020 presidential candidates, and then as the videos start to break, for example, we can analyze them and try to determine if we think they are real or not,” Agarwal told Berkeley News in June of this year.

  “基本思路是,我們可以建立有關這些世界各國領導人的軟生物識別模型,比如2020年總統候選人,然后倘若視頻開始失真,我們可以對它們進行分析,來確定它們的真實性,”阿加瓦爾今年6月向伯克利新聞表示。

  Although Agarwal’s models aren’t fullproof, since people in different circumstances might use different facial tics, Van de Weghe think companies could offer soft biometric signatures for identity verification purposes in the future. Such a signature could be something as well-known as eye scans or a full body scan.

  盡管考慮到不同的人在不同的環境下可能會呈現不同的面部抽搐,阿加瓦爾的模型并不完全可靠,但范德韋赫認為公司將來可以提供用于身份驗證的軟生物特征簽名,這種特征可能是眾所周知的眼睛掃描或全身掃描。

  “I think that’s the way forward: create bigger data sets in cooperation with academics and big tech companies,” Van de Weghe says. “And we as newsrooms should try and train people and build media literacy about deepfakes.”

  “我認為這是我們前進的方向:與學術界和大型科技公司合作,以創建更大的數據集。”范德韋赫表示,“作為新聞人,我們應該努力幫助媒體加深對深度偽造的了解。”

  Recently, Facebook and Microsoft teamed up with universities to launch the Deepfake Detection Challenge. Another notable effort is the Defense Advanced Research Projects Agency’s (DARPA) goal of tackling deepfakes with semantic forensics, which looks for algorithmic errors that create, for instance, mismatched earrings worn by a person in a deepfake video. And in September 2018, the AI Foundation raised $10 million to create a tool that identifies deepfakes and other malicious content through both machine learning and human moderators.

  最近,Facebook和微軟與大學聯手發起了深度換臉檢測挑戰。另一個值得注意的努力是國防高級研究計劃局的目標,即用語義鑒證法來處理深度換臉贗品,尋找造成錯誤的算法。例如,一個人在深度換臉視頻中戴了與其不相配的耳環。而在2018年9月,人工智能基金會籌集了1000萬美元,通過機器學習和人類調解員創建了一個識別深度換臉和其他惡意內容的工具。

  But, Fast.AI’s Thomas remains skeptical that technology can fully solve the problem of deepfakes, whatever form they might take. She sees value in creating better systems for identifying deepfakes but reiterates that other types of misinformation are already rampant. Thomas says stakeholders should explore the social and psychological factors that play into deepfakes and other misinformation as well.

  但是,托馬斯仍然懷疑技術是否能完全解決深度換臉的問題,不管它們采取什么形式。她認為建立更好的系統來識別深度換臉是有價值的,但她重申,其他類型的錯誤信息也很猖獗。托馬斯說,利益相關者應該探索社會和心理因素,因為這些因素也會導致嚴重的深度換臉和其他錯誤信息。

  WHY IT’S TOUGH TO REGULATE DEEPFAKES為什么對深度換臉的監管難度很大

  Thomas, Van de Weghe, and Farid all agree that governments will have to step in and regulate deepfake technology because social media platforms, which amplify such incendiary content, are either unable or unwilling to police their own content.

  托馬斯、范德韋赫和法里德一致認為,政府將不得不介入并監管深度換臉技術,因為放大此類煽動性內容的社交媒體平臺要么無力監管,要么不愿意監管自己的內容。

  In June, Rep. Adam Schiff (D-CA), chair of the House Intelligence Committee, held the first hearing on the misinformation and disinformation threats posed by deepfakes. In his opening remarks, Schiff made note of how tech companies responded differently to the fake Pelosi video. YouTube immediately deleted the slowed-down video, while Facebook labeled it false and throttled back the speed at which it spread across the platform. These disparate reactions led Schiff to demand social media companies establish policies to remedy the upload and spread of deepfakes.

  今年6月,眾議院情報委員會主席、民主黨眾議員亞當·希夫就深度換臉技術造成的虛假信息和虛假信息威脅舉行了首次聽證會。希夫在開場白中指出,科技公司對此前的假視頻做出了不同的反應。YouTube立即刪除了這段慢速播放的視頻,而Facebook將其標注為假,并限制了它在整個平臺上的傳播速度。這些不同的反應導致希夫要求社交媒體公司制定政策,糾正深度換臉視頻的上傳和傳播。

  “In the short-term, promoting disinformation and other toxic, incendiary content is profitable for the major platforms, so we have a total misalignment of incentives,” says Fast.AI’s Thomas. “I don’t think that the platforms should be held liable for content that they host, but I do think they should be held liable for content they actively promote (e.g. YouTube recommended Alex Jones’ videos 16 billion times to people who weren’t even looking for him).”

  “在短期內,推廣虛假信息和其他有害的、煽動性的內容對這些平臺來說是有利可圖的,因此我們的激勵機制是完全錯位的。”托馬斯表示,“我不認為這些平臺應該對它們所承載的內容承擔責任,但我確實認為它們應該對它們積極推廣的內容承擔責任(例如,YouTube將亞歷克斯?瓊斯的視頻推薦給那些甚至沒有在尋找他的人160億次)。”

  “And, in general, I think it can be helpful to consider how we’ve [legislatively] dealt with other industries that externalize large costs to society while privately claiming the profits (such as industrial pollution, big tobacco, and fast food/junk food),” Thomas adds.

  托馬斯補充道:“總的來說,我認為,考慮一下我們如何通過立法來處理那些將巨額社會成本外部化、同時私下要求利潤的其它行業(如工業污染、大型煙草和快餐/垃圾食品),是有幫助的。”

  Deeptrace Labs’ Patrini says regulation of synthetic media could prove complicated. But, he believes some current laws, like those covering defamation, libel, and copyright, could be used to police malicious deepfakes. A blanket law to stop deepfakes would be misguided, says Patrini. Instead, he advocates government support for synthetic media applications that benefit society, while funding research into creating tools to detect deepfakes and encouraging startups and other companies to do the same.

  帕特里尼表示,對合成媒體的監管可能會變得很復雜。但是他也認為,目前的一些法律,比如那些涉及中傷、誹謗和版權的法律,可以用來監管惡意的深度換臉。帕特里尼說,出臺一項全面禁止深度換臉的法律將是錯誤的行為。相反,他主張政府支持有利于社會的合成媒體應用,同時資助研究開發檢測深度換臉的工具,并鼓勵初創企業和其他公司也這么做。

  “[Government] can also educate citizens that this technology is already here and that we need to retrain our ears and eyes to not believe everything we see and hear on the internet,” says Patrini. “We need to inoculate people and society instead of repairing things in maybe two years when something very catastrophic or controversial might happen because of misuse of this technology.”

  “政府還可以教育公民這種技術的存在,因此我們需要重新訓練我們的耳朵和眼睛,不要相信我們在互聯網上看到和聽到的一切。”帕特里尼說:“我們需要給人們和社會先打好預防針,而不是在可能兩年后因為濫用這項技術而發生非常災難性或有爭議的事情時才亡羊補牢。”

  Ommer says computer vision researchers are well aware of the malicious applications of deepfakes. And he sees a role for government to play in creating accountability for how deepfakes are used.

  奧默表示,計算機視覺研究人員很清楚深度換臉技術的惡意應用。他認為政府應該為如何使用深度換臉技術建立問責制。

  “We all see applications of image understanding and the benefits that it can potentially have,” says Ommer. “A very important part of this is responsibility and who will take a share in this responsibility? Government agencies and so on who have interviewed me obviously see their share in this responsibility. Companies say and probably—in the interest of their stockholders—have to say that they see their responsibility; but, we all know how they have handled this responsibility up until now.”

  “我們都看到了圖像理解的應用,以及它可能帶來的好處,”奧默說,“但其中一個非常重要的部分是要明確承擔哪些責任,以及誰將承擔這一責任?采訪過我的政府機構等顯然看到他們也負有這一責任。公司也許為了股東的利益,他們可能也不得不表示他們看到了自己的責任;但是,到目前為止,我們心里都很清楚他們是如何處理這一責任的。”

  “It’s a tricky thing,” Ommer says. “Just hoping that this will all go away . . . it won’t.”

  “這是一件很棘手的事情,”奧默接著表示,“只是希望這一切都會過去……但是我們知道它將愈演愈烈。”

?

  注:《譯科技 | 什么?!有人模仿你的臉,還有人模仿你全身?》來源于FastCompany(點擊查看原文)。本文系數據觀原創編譯,譯者數據觀/王婕,轉載請務必注明譯者和來源。

責任編輯:張薇

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