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42:["$","$L45",null,{"hasPartnerEndpoint":false}] 4c:Ta39,# Model Overview ## Description: Hive's Deepfake Image Detection model identifies whether or not an image is a deepfake. The model locates faces in an image. For each detected face, this model outputs a bounding box for its location, a classification, and accompanying confidence score. Image classification model that classifies whether an image is a deepfake. This model is ready for commercial and non-commercial use.
## Third-Party Community Consideration This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case. ### License/Terms of Use: https://thehive.ai/terms-of-use ## References (Leave Blank If None): ## Model Architecture: **Architecture Type:** Convolution Neural Network (CNN)]
**Network Architecture:** EfficientNet-B4, YoloV8
## Input: **Input Type(s):** Ensemble Gradient Boosted (EGB) Image
**Input Format(s):** PNG, JPEG, or JPG
**Input Parameters:** Two-Dimensional (2D)
## Output: **Output Type(s):** Image with bounding boxes, Text
**Output Format:** JPEG, String
**Output Parameters:** Two-Dimensional (2D) Image with the same shape as input, One Dimensional (1D)
## Software Integration: **Supported Hardware Microarchitecture Compatibility:**
* NVIDIA Ampere
* NVIDIA Jetson
* NVIDIA Hopper
* NVIDIA Lovelace
* NVIDIA Volta
**[Preferred/Supported] Operating System(s):** Linux
## Model Version(s): v1.0
# Training Dataset: ## Training Dataset: ** Data Collection Method by dataset
* Unknown
** Labeling Method by dataset
* Unknown
**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Trained on a large dataset comprising millions of artificially generated images and human-created images such as photographs, digital and traditional art, and memes sourced from across the web.
## Inference: **Engine:** TensorRT
**Test Hardware:** L40
## Ethical Considerations (For NVIDIA Models Only): NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).4d:T810,import fs from 'fs'; import fetch from 'node-fetch'; const header_auth = "Bearer $NVIDIA_API_KEY"; const invoke_url = "https://ai.api.nvidia.com/v1/cv/hive/deepfake-image-detection" const input_image_path = "<% if(request.image_name) { %><%-request.image_name.replaceAll('"', '\\"')%><% } else { %>YOUR_IMAGE_FILE.png<% } %>" async function upload_asset(input, description) { const assets_url = "https://api.nvcf.nvidia.com/v2/nvcf/assets"; const headers = { "Authorization": header_auth, "Content-Type": "application/json", "accept": "application/json", }; const s3_headers = { "x-amz-meta-nvcf-asset-description": description, "content-type": "image/jpeg", }; const payload = { "contentType": "image/jpeg", "description": description }; const response = await fetch( assets_url, { method: 'POST', body: JSON.stringify(payload), headers: headers } ); const data = await response.json(); const asset_url = data["uploadUrl"]; const asset_id = data["assetId"]; const fileData = fs.readFileSync(input); await fetch( asset_url, { method: 'PUT', body: fileData, headers: s3_headers } ); return asset_id.toString(); } const data = fs.readFileSync(input_image_path); const imageB64 = Buffer.from(data).toString('base64'); var payload; var headers; if (imageB64.length < 180_000) { payload = { "input": [`data:image/jpeg;base64,${imageB64}`] }; headers = { "Content-Type": "application/json", "Authorization": header_auth, "Accept": "application/json" }; } else { const asset_id = await upload_asset(input_image_path, "Input Image"); payload = { "input": [`data:image/jpeg;asset_id,${asset_id}`] }; headers = { "Content-Type": "application/json", "NVCF-INPUT-ASSET-REFERENCES": asset_id, "Authorization": header_auth }; } const response = await fetch(invoke_url, { method: 'POST', body: JSON.stringify(payload), headers: headers }); const output = await response.json(); console.log(JSON.stringify(output)); 4e:T6d9,import requests, base64 header_auth = f"Bearer $NVIDIA_API_KEY" invoke_url = "https://ai.api.nvidia.com/v1/cv/hive/deepfake-image-detection" input_image_path = "<% if(request.image_name) { %><%-request.image_name.replaceAll('"', '\\"')%><% } else { %>YOUR_IMAGE_FILE.png<% } %>" def upload_asset(path, desc): assets_url = "https://api.nvcf.nvidia.com/v2/nvcf/assets" headers = { "Content-Type": "application/json", "Authorization": header_auth, "accept": "application/json", } payload = { "contentType": "image/png", "description": desc } response = requests.post(assets_url, headers=headers, json=payload, timeout=30) print("assets response", response, flush=True) current_pre_signed_url = response.json()["uploadUrl"] asset_id = response.json()["assetId"] headers = { "Content-Type": "image/png", "x-amz-meta-nvcf-asset-description": desc, } input_data = open(path, "rb") response = requests.put( current_pre_signed_url, data=input_data, headers=headers, timeout=300, ) return asset_id with open(input_image_path, "rb") as f: image_b64 = base64.b64encode(f.read()).decode() if len(image_b64) < 180_000: payload = { "input": [f"data:image/png;base64,{image_b64}"] } headers = { "Content-Type": "application/json", "Authorization": header_auth, "Accept": "application/json", } else: asset_id = upload_asset(input_image_path, "Input Image") payload = { "input": [f"data:image/png;asset_id,{asset_id}"] } headers = { "Content-Type": "application/json", "NVCF-INPUT-ASSET-REFERENCES": asset_id, "Authorization": header_auth, } response = requests.post(invoke_url, headers=headers, json=payload) print(response.json()) 4f:T6f7,## Step 1. Generate API Key ::generate-api-key ## Step 2. Pull and Run the NIM ```bash $ docker login nvcr.io Username: $oauthtoken Password: ``` Get the credentials to download the models from [Hive](https://thehive.ai/nvidia-nim-integration) and export them: ```bash export NIM_REPOSITORY_OVERRIDE="s3://..." export AWS_REGION="..." export AWS_ACCESS_KEY_ID="..." export AWS_SECRET_ACCESS_KEY="..." ``` Pull and run the NVIDIA NIM with the command below. ```bash # Create the cache directory on the host machine. export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" chmod 777 $LOCAL_NIM_CACHE # Run the container with the cache directory as a volume mount. docker run -it --rm --name=nim-server \ --runtime=nvidia \ --gpus='"device=0"' \ -e NIM_REPOSITORY_OVERRIDE \ -e AWS_REGION \ -e AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY \ -e NIM_HTTP_API_PORT=8003 \ -p 8003:8003 \ -p 8002:8002 \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \ nvcr.io/nim/hive/deepfake-image-detection:latest ``` ## Step 3. Test the NIM You can now make a local API call using this curl command: ```bash invoke_url="http://localhost:8003/v1/infer" input_image_path="input.jpg" # download an example image curl https://assets.ngc.nvidia.com/products/api-catalog/deepfake-image-detection/input/deepfake.jpg > $input_image_path image_b64=$(base64 $input_image_path) length=${#image_b64} echo '{ "input": ["data:image/png;base64,'${image_b64}'"], "return_image": false }' > payload.json curl $invoke_url \ -H "Content-Type: application/json" \ -d @payload.json ``` For more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/multimodal-safety/latest/getting-started.html). 43:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L46",null,{"alt":"","className":"object-cover object-center !left-1/2 z-10 !w-1/2 brightness-20 lg:brightness-75","fill":true,"priority":true,"src":"https://assets.ngc.nvidia.com/products/api-catalog/images/deepfake-image-detection.jpg","style":{"maskImage":"linear-gradient(to left, rgba(0,0,0,1), rgba(0,0,0,0))"}}],["$","div",null,{"className":"z-30 mx-auto flex w-full max-w-screen-xl min-w-0 flex-1 flex-col items-start justify-between gap-md lg:flex-row lg:gap-ml","children":["$","section",null,{"aria-describedby":"resource-description","aria-labelledby":"resource-name","className":"flex w-full min-w-0 flex-col lg:flex-1","children":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L47",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/hive","children":"Hive"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"deepfake-image-detection"}],[["$","span","DEPRECATION",{"className":"nv-badge nv-badge--kind-solid nv-badge--color-red h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Deprecated"]}],["$","span","AVAILABLE",{"className":"nv-badge nv-badge--kind-solid h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Downloadable"]}]],"$undefined"]}],["$","p",null,{"className":"max-w-[85ch] text-ms text-primary","id":"resource-description","children":"Advanced AI model detects faces and identifies deep fake images."}],["$","div",null,{"className":"flex w-full flex-col items-start gap-md lg:flex-row lg:items-end lg:justify-between","children":[["$","div",null,{"className":"flex w-full min-w-0 flex-wrap gap-sm lg:flex-1","children":["$","$L48",null,{"artifactType":"ENDPOINT","density":"standard","labels":["AI safety","Content moderation","computer vision","deep fake detection","nimType:endpoint:nim_type_run_anywhere"],"searchRoutePath":"/models"}]}],["$","div",null,{"className":"z-30 flex w-full flex-row justify-start gap-ms sm:justify-end lg:w-auto lg:shrink-0","children":[["$","$L49",null,{"cta":{"text":"Get License","url":"https://thehive.ai/nvidia-nim-integration"},"inverse":true,"tone":"secondary"}],["$","$L4a",null,{"cta":{"text":"Get License","url":"https://thehive.ai/nvidia-nim-integration"},"isNIMAccessRestricted":"$undefined","isNIMAvailable":true}]]}]]}]]}]}]}],["$","div",null,{"className":"pointer-events-none absolute right-0 bottom-0 left-0 z-20 h-30 bg-gradient-to-t to-transparent from-manitoulinLightBlack via-manitoulinLightBlack/70"}]]}],["$","div",null,{"className":"px-4 mx-auto flex max-w-screen-xl flex-col pt-0 lg:px-0","data-body-background":"light-black","children":["$","section",null,{"className":"flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack sm:rounded-6 lg:rounded-8 p-ms lg:p-lg","children":["$","$L4b",null,{"artifact":{"artifactType":"ENDPOINT","name":"deepfake-image-detection","displayName":"deepfake-image-detection","publisher":"hive","shortDescription":"Advanced AI model detects faces and identifies deep fake images.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/deepfake-image-detection.jpg","labels":"$43:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:0:props:children:props:labels","attributes":[{"key":"AVAILABLE","value":"true"},{"key":"DEPRECATION","value":"04/15/2026"},{"key":"PREVIEW","value":"false"}],"updatedDate":"2025-03-22T01:54:47.100Z","canGuestDownload":true,"createdDate":"2024-11-13T05:12:38.874Z","description":"$4c","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy"},"endpointName":"deepfake-image-detection","endpointSpec":{"openAPISpec":{"openapi":"3.1.0","info":{"contact":{"name":"NVIDIA Support","url":"https://help.nvidia.com/"},"description":"The NVIDIA NIM REST API. 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