本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 管制內容
您可以使用 Amazon Rekognition 偵測不適當、不需要或冒犯性的內容。您可以在社交媒體、廣播媒體、廣告和電子商務環境中使用 Rekognition 管制 API，以建立更安全的使用者體驗、為廣告客戶提供品牌安全保證，並遵守當地和全球法規。
如今，許多公司完全依賴人類審核員來審查第三方或使用者生成的內容，而其他公司則只是對使用者投訴做出反應，以消除令人反感或不適當的映像、廣告或影片。但是，人類審核員無法以充分的質量或速度進行擴展来滿足這些需求，這會導致使用者體驗不佳，實現規模的高成本，甚至損失品牌聲譽。透過使用 Rekognition 審核映像和影片，人類審核員可以檢閱較少內容，通常是機器學習標記的總體容量的 1% 至 5%。這使人類審核員能夠專注於更有價值的活動，並且仍然以現有成本的一小部分完成所有内容管制。若要組建人類審核員並執行人工審核任務，您可以使用已與 Rekognition 整合的 Amazon 增強版 AI。
您可以使用自訂管制特徵來增強協調深度學習模型的準確性。使用自訂管制，您可以上傳映像並註解這些映像來訓練自訂管制轉接器。接著，可將經過訓練的轉接器提供給 [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html) 操作，以增強其映像方面的效能。如需詳細資訊，請參閱 [透過自訂管制提升準確性](moderation-custom-moderation.md)。
**Rekognition 內容管制操作支援的標籤**
+ 若要下載管制標籤清單，請[按一下此處](samples/rekognition-moderation-labels.zip)。
**Topics**
+ [使用映像和影片管制 API](moderation-api.md)
+ [測試內容管制第 7 版並轉換 API 回應](moderation-response-transform.md)
+ [偵測不適當的映像](procedure-moderate-images.md)
+ [偵測不當儲存的影片](procedure-moderate-videos.md)
+ [透過自訂管制提升準確性](moderation-custom-moderation.md)
+ [使用 Amazon 增強版 AI 檢閱不適當的內容。](a2i-rekognition.md)
下圖顯示呼叫操作的順序，取決於您使用內容管制的影像或影片元件的目標：
![\[描繪影像和影片管制步驟的流程圖。\]](http://docs.aws.amazon.com/zh_tw/rekognition/latest/dg/images/Moderation workflow.png)
# 使用映像和影片管制 API
在 Amazon Rekognition Image API 中，您可以使用 [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html) 同步偵測不適當、不想要或令人反感的內容，並以非同步方式使用 [StartMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartMediaAnalysisJob.html) 和 [GetMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetMediaAnalysisJob.html) 操作。您可以使用 Amazon Rekognition Video API，透過 [StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html) 和 [GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html) 操作，以非同步方式偵測此類內容。
## 標籤類別
Amazon Rekognition 使用三層階層分類法來標記不適當、不想要或令人反感的內容類別。具有分類等級 1 (L1) 的每個標籤都有數個分類等級 2 標籤 (L2)，而某些分類等級 2 標籤可能具有分類等級 3 標籤 (L3)。這允許對內容進行階層分類。
對於每個偵測到的管制標籤，API 也會傳回 `TaxonomyLevel`，其中包含標籤所屬的關卡 (1、2 或 3)。例如，影像可能會根據下列分類進行標記：
L1：私密組件和 Kissing 的非明確 Nudity，L2：非明確 Nudity，L3：隱含 Nudity。
**注意**
我們建議您使用 L1 或 L2 類別來調整您的內容，並只使用 L3 類別來移除您不想調整的特定概念 （即偵測您可能不想根據您的管制政策分類為不適當、不想要或令人反感的內容）。
下表顯示類別層級與每個層級可能標籤之間的關係。若要下載管制標籤清單，請[按一下此處](samples/rekognition-moderation-labels.zip)。
|
|
| 最上層類別 (L1) | 二級類別 (L2) | 第三層類別 (L3) | 定義 |
| --- |--- |--- |--- |
| Explicit | Explicit Nudity | Exposed Male Genitalia | Human male genitalia, including the penis (whether erect or flaccid), the scrotum, and any discernible pubic hair. This term is applicable in contexts involving sexual activity or any visual content where male genitals are displayed either completely or partially. |
| Exposed Female Genitalia | External parts of the female reproductive system, encompassing the vulva, vagina, and any observable pubic hair. This term is applicable in scenarios involving sexual activity or any visual content where these aspects of female anatomy are displayed either completely or partially. |
| Exposed Buttocks or Anus | Human buttocks or anus, including instances where the buttocks are nude or when they are discernible through sheer clothing. The definition specifically applies to situations where the buttocks or anus are directly and completely visible, excluding scenarios where any form of underwear or clothing provides complete or partial coverage. |
| Exposed Female Nipple | Human female nipples, including fully visible and partially visible aerola (area surrounding the nipples) and nipples. |
| Explicit Sexual Activity | N/A | Depiction of actual or simulated sexual acts which encompasses human sexual intercourse, oral sex, as well as male genital stimulation and female genital stimulation by other body parts and objects. The term also includes ejaculation or vaginal fluids on body parts and erotic practices or roleplaying involving bondage, discipline, dominance and submission, and sadomasochism. |
| Sex Toys | N/A | Objects or devices used for sexual stimulation or pleasure, e.g., dildo, vibrator, butt plug, beats, etc. |
| Non-Explicit Nudity of Intimate parts and Kissing | Non-Explicit Nudity | Bare Back | Human posterior part where the majority of the skin is visible from the neck to the end of the spine. This term does not apply when the individual's back is partially or fully occluded. |
| Exposed Male Nipple | Human male nipples, including partially visible nipples. |
| Partially Exposed Buttocks | Partially exposed human buttocks. This term includes a partially visible region of the buttocks or butt cheeks due to short clothes, or partially visible top portion of the anal cleft. The term does not apply to cases where the buttocks is fully nude. |
| Partially Exposed Female Breast | Partially exposed human female breast where one a portion of the female's breast is visible or uncovered while not revealing the entire breast. This term applies when the region of the inner breast fold is visible or when the lower breast crease is visible with nipple fully covered or occluded. |
| Implied Nudity | An individual who is nude, either topless or bottomless, but with intimate parts such as buttocks, nipples, or genitalia covered, occluded, or not fully visible. |
| Obstructed Intimate Parts | Obstructed Female Nipple | Visual depiction of a situation in which a female's nipples is covered by opaque clothing or coverings, but their shapes are clearly visible. |
| Obstructed Male Genitalia | Visual depiction of a situation in which a male's genitalia or penis is covered by opaque clothing or coverings, but its shape is clearly visible. This term applies when the obstructed genitalia in the image is in close-up. |
| Kissing on the Lips | N/A | Depiction of one person's lips making contact with another person's lips. |
| Swimwear or Underwear | Female Swimwear or Underwear | N/A | Human clothing for female swimwear (e.g., one-piece swimsuits, bikinis, tankinis, etc.) and female underwear (e.g., bras, panties, briefs, lingerie, thongs, etc.) |
| Male Swimwear or Underwear | N/A | Human clothing for male swimwear (e.g., swim trunks, boardshorts, swim briefs, etc.) and male underwear (e.g., briefs, boxers, etc.) |
| Violence | Weapons | N/A | Instruments or devices used to cause harm or damage to living beings, structures, or systems. This includes firearms (e.g., guns, rifles, machine gunes, etc.), sharp weapons (e.g., swords, knives, etc.), explosives and ammunition (e.g., missile, bombs, bullets, etc.). |
| Graphic Violence | Weapon Violence | The use of weapons to cause harm, damage, injury, or death to oneself, other individuals, or properties. |
| Physical Violence | The act of causing harm to other individuals or property (e.g., hitting, fighting, pulling hair, etc.) or other act of violence involving crowd or multiple individuals. |
| Self-Harm | The act of causing harm to oneself, often by cutting body parts such as arms or legs, where cuts are typically visible. |
| Blood & Gore | Visual representation of violence on a person, a group of individuals, or animals, involving open wounds, bloodshed, and mutilated body parts. |
| Explosions and Blasts | Depiction of a violent and destructive burst of intense flames with thick smoke or dust and smoke erupting from the ground. |
| Visually Disturbing | Death and Emaciation | Emaciated Bodies | Human bodies that are extremely thin and undernourished with severe physical wasting and depletion of muscle and fat tissue. |
| Corpses | Human corpses in the form of mutilated bodies, hanging corpses, or skeletons. |
| Crashes | Air Crash | Incidents of air vehicles, such as airplanes, helicopters, or other flying vehicles, resulting in damage, injury, or death. This term applies when parts of the air vehicles are visible. |
| Drugs & Tobacco | Products | Pills | Small, solid, often round or oval-shaped tables or capsules. This term applies to pills presented as standalones, in a bottle, or a transparent packet and does not apply to a visual depiction of a person taking pills. |
| Drugs & Tobacco Paraphernalia & Use | Smoking | The act of inhaling, exhaling, and lighting up burning substances including cigarettes, cigars, e-cigarettes, hookah, or joint. |
| Alcohol | Alcohol Use | Drinking | The act of drinking alcoholic beverages from bottles or glasses of alcohol or liquor. |
| Alcoholic Beverages | N/A | Close up of one or multiple bottles of alcohol or liquor, glasses or mugs with alcohol or liquor, and glasses or mugs with alcohol or liquor held by an individual. This term does not apply to an individual drinking from bottles or glasses of alcohol or liquor. |
| Rude Gestures | Middle Finger | N/A | Visual depiction of a hand gesture with middle finger is extended upward while the other fingers are folded down. |
| Gambling | N/A | N/A | The act of participating in games of chance for a chance to win a prize in casinos, e.g., playing cards, blackjacks, roulette, slot machines at casinos, etc. |
| Hate Symbols | Nazi Party | N/A | Visual depiction of symbols, flags, or gestures associated with Nazi Party. |
| White Supremacy | N/A | Visual depiction of symbols or clothings associated with Ku Klux Klan (KKK) and images with confederate flags. |
| Extremist | N/A | Images containing extremist and terrorist group flags. |
不是 L2 類別中的每個標籤都有 L3 類別中支援的標籤。此外，"Products" 和 "Drug and Tobacco Paraphernalia and Use" L2 標籤下的 LL3 標籤並不詳盡。這些 L2 標籤涵蓋上述 L3 標籤以外的概念，在這種情況下，API 回應中只會傳回 L2 標籤。
您可以確定應用程式內容是否適合。例如，可接受映像本身具有暗示性內容，但不接受含有裸露內容。若要篩選映像，請使用 `DetectModerationLabels` (映像) 與 `GetContentModeration` (影片) 傳回的 [ModerationLabel](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ModerationLabel.html) 標籤陣列。
## 內容類型
API 也可以識別動畫或說明的內容類型，並在回應中傳回內容類型：
+ 動畫內容包括影片遊戲和動畫 （例如卡通、漫畫、魔術、動畫）。
+ 說明內容包括繪製、繪製和草圖。
## 可信度
您可指定 `MinConfidence` 輸入參數，以設定 Amazon Rekognition 用於偵測不安全內容的可信度閾值。系統不會對偵測到可信度比 `MinConfidence` 低的不安全內容傳回標籤。
為 指定`MinConfidence`小於 50% 的值可能會傳回大量誤報結果 （即更高的召回率、較低的精確度）。另一方面，指定`MinConfidence`高於 50% 的 可能會傳回較低的誤報結果數量 （即較低的召回率、更高的精確度）。如果您不指定 `MinConfidence` 的值，Amazon Rekognition 會以至少 50% 的可信度，傳回偵測到的不安全內容標籤。
`ModerationLabel` 陣列包含前述類別中的標籤，以及對已辨識內容之正確性所估計的可信度。最上層標籤會與任何已識別的第二層標籤一起傳回。例如，Amazon Rekognition 傳回的最上層標籤可能是具有高信賴分數的「露骨裸露」。這可能足以滿足您的篩選需求。不過，如果有必要，您也可以使用第二層標籤的可信度分數 (例如「男性裸露映像」)，來取得更精細的篩選。如需範例，請參閱 [偵測不適當的映像](procedure-moderate-images.md)。
## 版本控制
Amazon Rekognition Image 和 Amazon Rekognition Video 都會傳回用於偵測不適當內容的協調偵測模型版本 (`ModerationModelVersion`)。
## 排序和彙總
使用 GetContentModeration 檢索結果時，您可以對結果進行排序和彙總。
**排序順序**：傳回的標籤陣列會依時間排序。若要依標籤排序，請在 `SortBy` 輸入參數中指定 `NAME` 以執行 `GetContentModeration`。如果標籤在視訊中多次出現，將會有 `ModerationLabel` 元素的多個執行個體。
**標籤資訊**：ModerationLabels 陣列元素含有 `ModerationLabel` 物件，其中包括標籤名稱和 Amazon Rekognition 對於標籤偵測精確度的可信度分數。時間戳記是偵測到 `ModerationLabel` 的時間，定義為自影片開始以來經過的毫秒數。對於依影片 `SEGMENTS` 彙總的結果，會傳回 `StartTimestampMillis`、`EndTimestampMillis` 和 `DurationMillis` 的結構，分別定義區段的開始時間、結束時間和持續時間。
**彙總**：指定傳回時如何彙總結果。依據 `TIMESTAMPS` 彙總預設值。您也可以選擇依據 `SEGMENTS` 彙總，以便在時間範圍內彙總結果。僅傳回區段期間偵測到的標籤。
## 自訂管制轉接器狀態
自訂管制轉接器可以處於下列其中一種狀態：TRAINING\$1IN\$1PROGRESS、TRAINING\$1COMPLETED、TRAINING\$1FAILED、DELETING、DEPRECATED 或 EXPIRED。如需這些轉接器狀態的完整說明，請參閱[管理轉接器](https://docs.aws.amazon.com/rekognition/latest/dg/managing-adapters.html)。
**注意**
Amazon Rekognition 不是授權機構，也不會以任何方式聲稱是不適當或令人反感的內容的詳盡篩選條件。此外，映像和影片審核 API 不會偵測映像是否包含非法內容，例如兒童色情。
# 測試內容管制第 7 版並轉換 API 回應
Rekognition 將內容管制標籤偵測功能的影像視訊元件機器學習模型從 6.1 版更新為 7 版。此更新提高了整體準確性，並引入了幾個新類別以及修改其他類別。
如果您是 6.1 版的目前影片使用者，我們建議您採取下列動作，無縫轉換為 第 7 版：
1. 下載並使用 AWS 私有 SDK （請參閱 [AWS 內容管制版本 7 的 SDK 和使用者指南](moderation-labels-update-sdk.md)) 來呼叫 StartContentModeration API。
1. 檢閱 API 回應或主控台中傳回的標籤和可信度分數的更新清單。如有必要，請相應地調整您的應用程式後置處理邏輯。
1. 您的帳戶將保留在 6.1 版，直到 2024 年 5 月 13 日為止。如果您想要在 2024 年 5 月 13 日後使用 6.1 版，請在 2024 年 4 月 30 日之前聯絡 [AWS 支援團隊](https://aws.amazon.com/support)以請求延長。我們可以將您的帳戶延伸至 6.1 版，直到 2024 年 6 月 10 日為止。如果我們未在 2024 年 4 月 30 日之前收到您的回覆，您的帳戶將從 2024 年 5 月 13 日開始自動遷移至 7.0 版。
# AWS 內容管制版本 7 的 SDK 和使用者指南
下載與您選擇的開發語言對應的開發套件，並參閱適當的使用者指南。
|
|
| 連結至 SDK | 安裝/使用者指南 |
| --- |--- |
| [Java-1.X](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_java_v1.jar) | [指南 - Java 1.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_Java1.x.pdf) |
| [Java-2.X](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_java_v2.jar) | [指南 - Java 2.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_Java2.x.pdf) |
| [JavaScript v2](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_js_v2.tgz) | [指南 - JavaScript v2.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_JavaScriptV2.pdf) |
| [JavaScript v3](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_js_v3.zip) | [指南 - JavaScript v3.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_JavaScriptV3.pdf) |
| [Python](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_python.zip) | [指南 - Python 和 AWS CLI.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_Python.pdf) |
| [Ruby](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_ruby.zip) | [指南 - RubyV3.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_RubyV3.pdf) |
| [go\$1v1](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_go_v1.zip) | [指南 - GO V1.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_GoV1.pdf) |
| [go\$1v2](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_go_v2.zip) | [指南 - GO V2.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_GoV2.pdf) |
| [DotNet](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_dotnet.zip) | [指南 - .NET.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_DotNET.pdf) |
| [php](https://d1m67pwji3rslw.cloudfront.net/sdk/aws_rekognition_php.zip) | [指南 - PHP.pdf](https://d1m67pwji3rslw.cloudfront.net/guide/Guide_PHP.pdf) |
## 6.1 至 7 版的標籤映射
內容管制第 7 版新增了標籤類別，並修改了先前現有的標籤名稱。在決定如何將 6.1 標籤映射至 7 個標籤[標籤類別](moderation-api.md#moderation-api-categories)時，請參閱 的分類表。
下節提供一些標籤映射範例。我們建議您先檢閱這些映射和標籤定義，再根據應用程式的後置處理邏輯進行必要的更新。
**L1 映射結構描述**
如果您使用僅篩選最上層類別 (L1) 的後置處理邏輯 （例如 `Explicit Nudity`、 `Violence`等）`Suggestive`，請參閱下表以更新您的程式碼。
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| V6.1 L1 | V7 L1 |
| --- |--- |
| Explicit Nudity | Explicit |
| Suggestive | Non-Explicit Nudity of Intimate parts and Kissing |
| Swimwear or Underwear |
| Violence | Violence |
| Visually Disturbing | Visually Disturbing |
| Rude Gestures | Rude Gestures |
| Drugs | Drugs & Tobacco |
| Tobacco | Drugs & Tobacco |
| Alcohol | Alcohol |
| Gambling | Gambling |
| Hate Symbols | Hate Symbols |
**L2 映射結構描述**
如果您使用 L1 和 L2 類別 （例如 `Violence / Weapon Violence`等） 上篩選的後製處理邏輯`Explicit Nudity / Nudity, Suggestive / Female Swimwear Or Underwear`，請參閱下表以更新您的程式碼。
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| V6.1 L1 | V6.1 L2 | V7 L1 | V7 L2 | V7 L3 | V7 ContentTypes |
| --- |--- |--- |--- |--- |--- |
| Explicit Nudity | Nudity | Explicit | Explicit Nudity | 暴露的母乳頭 暴露的臀部或 Anus |
| Graphic Male Nudity | Explicit | Explicit Nudity | Exposed Male Genitalia |
| Graphic Female Nudity | Explicit | Explicit Nudity | Exposed Female Genitalia |
| Sexual Activity | Explicit | Explicit Sexual Activity | | |
| Illustrated Explicit Nudity | Explicit | Explicit Nudity | | Map to "Animated" and "Illustrated" |
| Illustrated Explicit Nudity | Explicit | Explicit Sexual Activity | | Map to "Animated" and "Illustrated" |
| Adult Toys | Explicit | Sex Toys | |
| Suggestive | Female Swimwear Or Underwear | Swimwear or Underwear | Female Swimwear or Underwear | |
| Male Swimwear Or Underwear | Swimwear or Underwear | Male Swimwear or Underwear | |
| Partial Nudity | Non-Explicit Nudity of Intimate parts and Kissing | Non-Explicit Nudity | Implied Nudity |
| Barechested Male | Non-Explicit Nudity of Intimate parts and Kissing | Non-Explicit Nudity | Exposed Male Nipple |
| Revealing Clothes | Non-Explicit Nudity of Intimate parts and Kissing | Non-Explicit Nudity | |
| Non-Explicit Nudity of Intimate parts and Kissing | Obstructed Intimate Parts | |
| Sexual Situations | Non-Explicit Nudity of Intimate parts and Kissing | Kissing on the Lips | |
| Violence | Graphic Violence Or Gore | Violence | Graphic Violence | Blood & Gore |
| Physical Violence | Violence | Graphic Violence | Physical Violence |
| Weapon Violence | Violence | Graphic Violence | Weapon Violence |
| Weapons | Violence | Weapons | |
| Self Injury | Violence | Graphic Violence | Self-Harm |
| Visually Disturbing | Emaciated Bodies | Visually Disturbing | Death and Emaciation | Emaciated Bodies |
| Corpses | Visually Disturbing | Death and Emaciation | Corpses |
| Hanging | Visually Disturbing | Death and Emaciation | Corpses |
| Air Crash | Visually Disturbing | Crashes | Air Crash |
| Explosions And Blasts | Violence | Graphic Violence | Explosions and Blasts |
| Rude Gestures | Middle Finger | Rude Gestures | Middle Finger | |
| Drugs | Drug Products | Drugs & Tobacco | Products | |
| Drug Use | Drugs & Tobacco | Drugs & Tobacco Paraphernalia & Use | |
| Pills | Drugs & Tobacco | Products | Pills |
| Drug Paraphernalia | Drugs & Tobacco | Drugs & Tobacco Paraphernalia & Use | |
| Tobacco | Tobacco Products | Drugs & Tobacco | Products | |
| Smoking | Drugs & Tobacco | Drugs & Tobacco Paraphernalia & Use | Smoking |
| Alcohol | Drinking | Alcohol | Alcohol Use | Drinking |
| Alcoholic Beverages | Alcohol | Alcoholic Beverages | |
| Gambling | Gambling | Gambling | | |
| Hate Symbols | Nazi Party | Hate Symbols | Nazi Party | |
| White Supremacy | Hate Symbols | White Supremacy | |
| Extremist | Hate Symbols | Extremist | |
# 偵測不適當的映像
您可以使用 [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html) 操作來判斷映像是否包含不適當或令人反感的內容。如需 Amazon Rekognition 中的管制標籤清單，請參閱[使用映像和影片管制 API](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api)。
## 偵測映像中的不適當內容
映像的格式必須是 .jpg 或 .png。您可以用映像位元組陣列 (Base64 編碼映像位元組) 的方式提供輸入映像，或指定 Amazon S3 物件。在這些程序中，您會將映像 (.jpg 或 .png) 上傳至 S3 儲存貯體。
若要執行這些程序，您需要安裝 AWS CLI 或適當的 AWS SDK。如需詳細資訊，請參閱[Amazon Rekognition 入門](getting-started.md)。您使用的 AWS 帳戶必須有 Amazon Rekognition API 的存取權限。如需詳細資訊，請參閱 [Amazon Rekognition 定義的動作](https://docs.aws.amazon.com/IAM/latest/UserGuide/list_amazonrekognition.html#amazonrekognition-actions-as-permissions)。
**偵測映像中的管制標籤 (SDK)**
1. 如果您尚未執行：
1. 建立或更新具有 `AmazonRekognitionFullAccess` 和 `AmazonS3ReadOnlyAccess` 權限的使用者。如需詳細資訊，請參閱[步驟 1：設定 AWS 帳戶並建立使用者](setting-up.md#setting-up-iam)。
1. 安裝和設定 AWS CLI 和 AWS SDKs。如需詳細資訊，請參閱[步驟 2：設定 AWS CLI 和 AWS SDKs](setup-awscli-sdk.md)。
1. 將映像上傳至您的 S3 儲存貯體。
如需指示說明，請參閱《Amazon Simple Storage Service 使用者指南》**中的[上傳物件至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html)。
1. 使用下列範例來呼叫 `DetectModerationLabels` 操作。
------
#### [ Java ]
此範例輸出偵測到的不適當內容標籤名稱、信心度以及偵測到的管制標籤的父標籤。
將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。
```
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
package aws.example.rekognition.image;
import com.amazonaws.services.rekognition.AmazonRekognition;
import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
import com.amazonaws.services.rekognition.model.DetectModerationLabelsRequest;
import com.amazonaws.services.rekognition.model.DetectModerationLabelsResult;
import com.amazonaws.services.rekognition.model.Image;
import com.amazonaws.services.rekognition.model.ModerationLabel;
import com.amazonaws.services.rekognition.model.S3Object;
import java.util.List;
public class DetectModerationLabels
{
public static void main(String[] args) throws Exception
{
String photo = "input.jpg";
String bucket = "bucket";
AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();
DetectModerationLabelsRequest request = new DetectModerationLabelsRequest()
.withImage(new Image().withS3Object(new S3Object().withName(photo).withBucket(bucket)))
.withMinConfidence(60F);
try
{
DetectModerationLabelsResult result = rekognitionClient.detectModerationLabels(request);
List labels = result.getModerationLabels();
System.out.println("Detected labels for " + photo);
for (ModerationLabel label : labels)
{
System.out.println("Label: " + label.getName()
+ "\n Confidence: " + label.getConfidence().toString() + "%"
+ "\n Parent:" + label.getParentName());
}
}
catch (AmazonRekognitionException e)
{
e.printStackTrace();
}
}
}
```
------
#### [ Java V2 ]
此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/DetectModerationLabels.java)的完整範例。
```
//snippet-start:[rekognition.java2.recognize_video_text.import]
//snippet-start:[rekognition.java2.detect_mod_labels.import]
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsRequest;
import software.amazon.awssdk.services.rekognition.model.DetectModerationLabelsResponse;
import software.amazon.awssdk.services.rekognition.model.ModerationLabel;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;
//snippet-end:[rekognition.java2.detect_mod_labels.import]
/**
* Before running this Java V2 code example, set up your development environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class ModerateLabels {
public static void main(String[] args) {
final String usage = "\n" +
"Usage: " +
" \n\n" +
"Where:\n" +
" sourceImage - The path to the image (for example, C:\\AWS\\pic1.png). \n\n";
if (args.length < 1) {
System.out.println(usage);
System.exit(1);
}
String sourceImage = args[0];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
detectModLabels(rekClient, sourceImage);
rekClient.close();
}
// snippet-start:[rekognition.java2.detect_mod_labels.main]
public static void detectModLabels(RekognitionClient rekClient, String sourceImage) {
try {
InputStream sourceStream = new FileInputStream(sourceImage);
SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
Image souImage = Image.builder()
.bytes(sourceBytes)
.build();
DetectModerationLabelsRequest moderationLabelsRequest = DetectModerationLabelsRequest.builder()
.image(souImage)
.minConfidence(60F)
.build();
DetectModerationLabelsResponse moderationLabelsResponse = rekClient.detectModerationLabels(moderationLabelsRequest);
List labels = moderationLabelsResponse.moderationLabels();
System.out.println("Detected labels for image");
for (ModerationLabel label : labels) {
System.out.println("Label: " + label.name()
+ "\n Confidence: " + label.confidence().toString() + "%"
+ "\n Parent:" + label.parentName());
}
} catch (RekognitionException | FileNotFoundException e) {
e.printStackTrace();
System.exit(1);
}
}
// snippet-end:[rekognition.java2.detect_mod_labels.main]
```
------
#### [ AWS CLI ]
此 AWS CLI 命令會顯示 CLI 操作的 JSON `detect-moderation-labels` 輸出。
將 `amzn-s3-demo-bucket` 與 `input.jpg` 取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。使用您開發人員設定檔的名稱取代 `profile_name` 的值。若要使用轉接器，請將專案版本的 ARN 提供給 `project-version` 參數。
```
aws rekognition detect-moderation-labels --image "{S3Object:{Bucket:,Name:}}" \
--profile profile-name \
--project-version "ARN"
```
如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\$1) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。例如，請參閱下列內容：
```
aws rekognition detect-moderation-labels --image "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"image-name\"}}" \
--profile profile-name
```
------
#### [ Python ]
此範例輸出偵測到的不適當或令人反感的內容標籤名稱、信心度以及偵測到的不安全內容標籤的父標籤。
在函數 `main` 中，將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。將建立 Rekognition 工作階段的行中 `profile_name` 值取代為您開發人員設定檔的名稱。
```
#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
import boto3
def moderate_image(photo, bucket):
session = boto3.Session(profile_name='profile-name')
client = session.client('rekognition')
response = client.detect_moderation_labels(Image={'S3Object':{'Bucket':bucket,'Name':photo}})
print('Detected labels for ' + photo)
for label in response['ModerationLabels']:
print (label['Name'] + ' : ' + str(label['Confidence']))
print (label['ParentName'])
return len(response['ModerationLabels'])
def main():
photo='image-name'
bucket='amzn-s3-demo-bucket'
label_count=moderate_image(photo, bucket)
print("Labels detected: " + str(label_count))
if __name__ == "__main__":
main()
```
------
#### [ .NET ]
此範例輸出偵測到的令人反感的內容標籤名稱、信心度以及偵測到的管制標籤的父標籤。
將 `amzn-s3-demo-bucket` 與 `photo` 的值取代為您在步驟 2 中所使用的 S3 儲存貯體名稱與映像檔案名稱。
```
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
using System;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
public class DetectModerationLabels
{
public static void Example()
{
String photo = "input.jpg";
String bucket = "amzn-s3-demo-bucket";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectModerationLabelsRequest detectModerationLabelsRequest = new DetectModerationLabelsRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
MinConfidence = 60F
};
try
{
DetectModerationLabelsResponse detectModerationLabelsResponse = rekognitionClient.DetectModerationLabels(detectModerationLabelsRequest);
Console.WriteLine("Detected labels for " + photo);
foreach (ModerationLabel label in detectModerationLabelsResponse.ModerationLabels)
Console.WriteLine("Label: {0}\n Confidence: {1}\n Parent: {2}",
label.Name, label.Confidence, label.ParentName);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
}
```
------
## DetectModerationLabels 操作要求
`DetectModerationLabels` 的輸入是映像。在此範例 JSON 輸入中，來源映像從 Amazon S3 儲存貯體載入。`MinConfidence` 是在偵測到的標籤的準確性中 Amazon Rekognition Image 必須具有的最低可信度，以便在回應中傳回。
```
{
"Image": {
"S3Object": {
"Bucket": "amzn-s3-demo-bucket",
"Name": "input.jpg"
}
},
"MinConfidence": 60
}
```
## DetectModerationLabel 操作回應
`DetectModerationLabels` 可從 S3 儲存貯體擷取輸入映像，您也可以用映像位元組的方式提供輸入映像。下列範例是來自 `DetectModerationLabels` 呼叫的回應。
在下列範例 JSON 回應中，請注意以下事項：
+ **不適當的映像偵測資訊**：此範例會顯示映像中發現不當或冒犯性內容的標籤清單。此清單包含最上層標籤與映像中偵測到的每個第二層標籤。
**標籤**：每個標籤都具有名稱、Amazon Rekognition 對標籤正確性估計的可信度，以及其父標籤的名稱。最上層標籤的父名稱為 `""`。
**標籤可信度**：每個標籤都有一個介於 0 到 100 之間的可信度值，該值表示 Amazon Rekognition 对標籤正確性估计的可信度百分比。您可以為要在 API 操作要求回應中傳回的標籤指定要求的可信度。
```
{
"ModerationLabels": [
{
"Confidence": 99.44782257080078,
"Name": "Smoking",
"ParentName": "Drugs & Tobacco Paraphernalia & Use",
"TaxonomyLevel": 3
},
{
"Confidence": 99.44782257080078,
"Name": "Drugs & Tobacco Paraphernalia & Use",
"ParentName": "Drugs & Tobacco",
"TaxonomyLevel": 2
},
{
"Confidence": 99.44782257080078,
"Name": "Drugs & Tobacco",
"ParentName": "",
"TaxonomyLevel": 1
}
],
"ModerationModelVersion": "7.0",
"ContentTypes": [
{
"Confidence": 99.9999008178711,
"Name": "Illustrated"
}
]
}
```
# 偵測不當儲存的影片
Amazon Rekognition Video 在儲存的影片中偵測不當或令人反感的內容是非同步操作。如要開始偵測不適當或令人反感的內容，請呼叫 [StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html)。Amazon Rekognition Video 向 Amazon Simple Notification Service 主題發佈影片的完成狀態。如果影片分析成功，請呼叫 [GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html) 以取得分析結果。如需開始影片分析並取得結果的詳細資訊，請參閱 [呼叫 Amazon Rekognition Video 操作](api-video.md)。如需 Amazon Rekognition 中的管制標籤清單，請參閱[使用映像和影片管制 API](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api)。
此程序會展開 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md) 中的程式碼，該操作使用 Amazon Simple Queue Service 佇列來取得影片分析要求的完成狀態。
**偵測 Amazon S3 儲存貯體 (SDK) 中存放的影片中的不當或令人反感的內容**
1. 執行 [使用 Java 或 Python (SDK) 分析儲存於 Amazon S3 儲存貯體中的影片](video-analyzing-with-sqs.md)。
1. 將下列程式碼新增至您在步驟 1 中建立的類別 `VideoDetect`。
------
#### [ Java ]
```
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
//Content moderation ==================================================================
private static void StartUnsafeContentDetection(String bucket, String video) throws Exception{
NotificationChannel channel= new NotificationChannel()
.withSNSTopicArn(snsTopicArn)
.withRoleArn(roleArn);
StartContentModerationRequest req = new StartContentModerationRequest()
.withVideo(new Video()
.withS3Object(new S3Object()
.withBucket(bucket)
.withName(video)))
.withNotificationChannel(channel);
StartContentModerationResult startModerationLabelDetectionResult = rek.startContentModeration(req);
startJobId=startModerationLabelDetectionResult.getJobId();
}
private static void GetUnsafeContentDetectionResults() throws Exception{
int maxResults=10;
String paginationToken=null;
GetContentModerationResult moderationLabelDetectionResult =null;
do{
if (moderationLabelDetectionResult !=null){
paginationToken = moderationLabelDetectionResult.getNextToken();
}
moderationLabelDetectionResult = rek.getContentModeration(
new GetContentModerationRequest()
.withJobId(startJobId)
.withNextToken(paginationToken)
.withSortBy(ContentModerationSortBy.TIMESTAMP)
.withMaxResults(maxResults));
VideoMetadata videoMetaData=moderationLabelDetectionResult.getVideoMetadata();
System.out.println("Format: " + videoMetaData.getFormat());
System.out.println("Codec: " + videoMetaData.getCodec());
System.out.println("Duration: " + videoMetaData.getDurationMillis());
System.out.println("FrameRate: " + videoMetaData.getFrameRate());
//Show moderated content labels, confidence and detection times
List moderationLabelsInFrames=
moderationLabelDetectionResult.getModerationLabels();
for (ContentModerationDetection label: moderationLabelsInFrames) {
long seconds=label.getTimestamp()/1000;
System.out.print("Sec: " + Long.toString(seconds));
System.out.println(label.getModerationLabel().toString());
System.out.println();
}
} while (moderationLabelDetectionResult !=null && moderationLabelDetectionResult.getNextToken() != null);
}
```
在函數 `main` 中，將下行:
```
StartLabelDetection(amzn-s3-demo-bucket, video);
if (GetSQSMessageSuccess()==true)
GetLabelDetectionResults();
```
取代為：
```
StartUnsafeContentDetection(amzn-s3-demo-bucket, video);
if (GetSQSMessageSuccess()==true)
GetUnsafeContentDetectionResults();
```
------
#### [ Java V2 ]
此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/VideoDetectInappropriate.java)的完整範例。
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class VideoDetectInappropriate {
private static String startJobId = "";
public static void main(String[] args) {
final String usage = """
Usage: