CommunityForensics-DeepfakeDet-ViT

ModelFree

image-classification model by undefined. 7,57,774 downloads.

Open Source

/ 100

5 capabilities

Capabilities5 decomposed

vision transformer-based deepfake detection via patch-level feature extraction

Medium confidence

Detects synthetic or manipulated faces in images using a Vision Transformer (ViT) architecture that divides input images into 16×16 pixel patches, embeds them through self-attention layers, and classifies the entire image as real or deepfake. The model is fine-tuned from timm/vit_small_patch16_384.augreg_in21k_ft_in1k, leveraging ImageNet-21k pre-training followed by ImageNet-1k fine-tuning, then adapted for forensic deepfake detection. Patch-based processing enables the model to detect subtle artifacts and inconsistencies across spatial regions that indicate synthetic generation or face-swapping.

Solves for

Detect whether a face image is a deepfake or authentic for content moderation systemsScreen user-uploaded profile pictures or video frames for synthetic faces in social platformsIdentify manipulated facial imagery in forensic investigations or fact-checking workflowsBuild automated pipelines to flag potentially synthetic media for human review

Best for

Content moderation teams building automated deepfake detection pipelines

Forensic analysts and fact-checkers verifying image authenticity

Social media platforms screening user-generated content at scale

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+ (via transformers library)

transformers library 4.30+

Limitations

Model trained on specific deepfake generation methods (likely GAN-based or face-swap tools from 2023-2024); may not generalize to novel synthesis techniques or future deepfake generators

Requires 384×384 pixel input resolution; lower-resolution or heavily compressed images may degrade detection accuracy

No temporal analysis — processes individual frames independently, cannot leverage video consistency cues that would improve detection in video deepfakes

What makes it unique

Leverages Vision Transformer patch-based self-attention architecture (ViT-Small with 384×384 resolution) pre-trained on ImageNet-21k then fine-tuned on ImageNet-1k, enabling detection of subtle spatial inconsistencies across image patches that indicate synthetic generation; differs from CNN-based detectors (e.g., EfficientNet) by capturing long-range dependencies and global context through multi-head attention rather than local convolutional receptive fields.

vs alternatives

ViT-based approach captures global facial inconsistencies through self-attention better than CNN-based deepfake detectors, and the 384×384 input resolution provides finer-grained patch analysis than smaller models, though it trades inference speed for detection accuracy compared to lightweight MobileNet-based alternatives.

batch image classification with safetensors model loading

Medium confidence

Loads pre-trained model weights from safetensors format (a safer, faster serialization than pickle) and processes multiple images sequentially or in batches through the ViT classifier, returning per-image predictions. The safetensors format eliminates arbitrary code execution risks during deserialization and enables memory-mapped weight loading for efficient inference on resource-constrained devices. Supports standard HuggingFace model loading patterns via the transformers library's AutoModelForImageClassification API.

Solves for

Load the deepfake detection model safely without pickle deserialization vulnerabilitiesProcess multiple images in a single inference pass for throughput optimizationDeploy the model on edge devices or low-memory environments using memory-mapped weightsIntegrate the model into existing HuggingFace-based ML pipelines without custom weight conversion

Best for

Production systems requiring safe model deserialization without code execution risks

Batch processing pipelines screening hundreds or thousands of images

Edge deployment scenarios (mobile, embedded systems) with memory constraints

Requires

transformers library 4.30+

safetensors library 0.3+

PyTorch 1.9+ or TensorFlow 2.6+

Limitations

Safetensors loading adds ~50-100ms overhead on first load due to format parsing, though subsequent loads are cached

Batch processing requires all images to be resized to 384×384, which may introduce artifacts for images with extreme aspect ratios

No built-in async/parallel processing — batches are processed sequentially on a single GPU/CPU

What makes it unique

Uses safetensors format for model deserialization, which is faster and safer than pickle (no arbitrary code execution), and integrates with HuggingFace's AutoModelForImageClassification API for zero-configuration model loading; enables memory-mapped weight access for efficient inference on resource-constrained devices.

vs alternatives

Safetensors loading is more secure and faster than pickle-based model formats used in older PyTorch checkpoints, and the HuggingFace integration eliminates manual weight conversion steps required for custom model architectures.

fine-tuned vit feature extraction for downstream forensic tasks

Medium confidence

Exposes intermediate layer activations from the fine-tuned ViT model, enabling extraction of learned forensic features that can be used for transfer learning, similarity search, or explainability analysis. The model's patch embeddings and transformer block outputs encode spatial patterns indicative of deepfake artifacts (e.g., blending boundaries, frequency inconsistencies, lighting anomalies), which can be leveraged by downstream classifiers or clustering algorithms without retraining the full model.

Solves for

Extract forensic feature embeddings from face images for similarity-based deepfake detection or clusteringUse learned representations as input to custom classifiers trained on domain-specific deepfake datasetsAnalyze which image regions or patches contribute most to deepfake predictions via attention visualizationBuild few-shot or zero-shot deepfake detectors by fine-tuning only the classification head on new data

Best for

Researchers developing novel deepfake detection methods using transfer learning

Teams building explainable AI systems that need to visualize which image regions trigger deepfake predictions

Organizations with domain-specific deepfake datasets wanting to adapt the model with minimal retraining

Requires

PyTorch 1.9+ with hooks API support

transformers library 4.30+

timm library 0.9+

Limitations

Feature extraction requires access to intermediate model layers; not all frameworks expose these cleanly (requires custom model wrapper or hook registration)

Extracted features are 384-dimensional (ViT-Small embedding size); dimensionality reduction may be needed for efficient similarity search

Attention visualization (e.g., attention rollout) is computationally expensive for 12-layer ViT; requires ~500ms per image on CPU

What makes it unique

Exposes ViT's multi-head self-attention and patch embeddings as forensic feature vectors, enabling downstream tasks to leverage learned spatial inconsistency patterns without full model retraining; the 384-dimensional [CLS] token embedding captures global deepfake indicators while patch-level embeddings preserve spatial localization for explainability.

vs alternatives

ViT feature extraction preserves spatial information through patch embeddings better than CNN-based feature extractors (which use spatial pooling), and the multi-head attention structure enables fine-grained explainability through attention rollout visualization, whereas CNN features are harder to interpret.

model inference with automatic device placement and mixed-precision support

Medium confidence

Automatically detects available hardware (GPU, CPU, TPU) and places the model and input tensors on the optimal device for inference. Supports mixed-precision inference (float16 on NVIDIA GPUs, bfloat16 on TPUs) via PyTorch's automatic mixed precision (AMP) context managers, reducing memory footprint by ~50% and accelerating inference by 2-3× on compatible hardware while maintaining classification accuracy through careful rounding.

Solves for

Deploy the deepfake detector on diverse hardware (cloud GPUs, edge devices, CPU-only servers) without manual device configurationReduce inference latency and memory usage on resource-constrained devices via mixed-precision inferenceScale batch processing on limited-memory GPUs by enabling lower-precision weightsEnsure reproducible inference across different hardware configurations

Best for

Production systems deployed across heterogeneous hardware (cloud, on-prem, edge)

Mobile or embedded applications requiring low-latency inference

Cost-sensitive deployments using cheaper GPU instances with limited VRAM

Requires

PyTorch 1.6+ (for AMP support)

CUDA 11.0+ (for GPU inference)

transformers library 4.30+

Limitations

Mixed-precision inference may introduce 0.5-2% accuracy degradation on edge cases due to float16 rounding; requires validation on target dataset

NVIDIA GPU support for mixed-precision is mature; AMD and Intel GPU support is experimental and may have compatibility issues

Automatic device placement assumes single-GPU or single-device scenarios; multi-GPU distributed inference requires manual configuration

What makes it unique

Integrates PyTorch's automatic mixed precision (torch.cuda.amp) with HuggingFace's device_map API to transparently optimize inference across CPU, GPU, and TPU without manual configuration; automatically selects float16 on NVIDIA GPUs and bfloat16 on TPUs while maintaining numerical stability through gradient scaling.

vs alternatives

Automatic device placement and mixed-precision support reduce deployment friction compared to manual device management in raw PyTorch, and the integration with HuggingFace transformers ensures compatibility with the broader ecosystem; provides 2-3× speedup on GPUs compared to float32 inference with minimal accuracy loss.

community-contributed model weights with mit licensing and version tracking

Medium confidence

The model is published under MIT license on HuggingFace Model Hub with full version history, enabling community contributions, reproducibility, and commercial use without licensing restrictions. The model card includes training details, dataset information, and performance metrics, and the safetensors format ensures transparent weight inspection. Version control via HuggingFace's git-based model repository allows tracking of model iterations and enables rollback to previous versions.

Solves for

Use a community-vetted deepfake detection model in commercial products without licensing concernsContribute improvements or alternative training approaches to the model via pull requestsReproduce the model's training and evaluation using published methodology and hyperparametersTrack model evolution and understand how performance has improved across versions

Best for

Commercial teams building deepfake detection features without licensing overhead

Open-source projects requiring permissive model licensing

Researchers studying deepfake detection trends and community contributions

Requires

HuggingFace account (free) for model access and contribution

Git knowledge for contributing improvements via pull requests

Internet connection for downloading model weights from HuggingFace Hub

Limitations

Community-contributed models may lack formal validation or peer review; accuracy claims should be independently verified

Model updates are not guaranteed to be backward-compatible; version pinning is required for reproducibility

HuggingFace Model Hub availability depends on external infrastructure; no guarantee of long-term hosting

What makes it unique

Published as a community-contributed model on HuggingFace Model Hub under MIT license with full git-based version history, enabling transparent model evolution, commercial use without licensing friction, and community contributions via pull requests; safetensors format ensures weights are inspectable and not obfuscated.

vs alternatives

MIT licensing and community hosting on HuggingFace eliminates licensing complexity compared to proprietary deepfake detectors, and the open-source approach enables community auditing and contributions, whereas commercial alternatives (e.g., AWS Rekognition, Microsoft Azure) require vendor lock-in and per-API-call pricing.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with CommunityForensics-DeepfakeDet-ViT, ranked by overlap. Discovered automatically through the match graph.

Model50

vit-base-patch16-224

image-classification model by undefined. 46,09,546 downloads.

patch-based image classification with vision transformer architecturefeature extraction and embedding generation for downstream tasksfine-tuning on custom image datasets with transfer learning

3 shared capabilities

Model41

vit-large-patch16-384

image-classification model by undefined. 4,74,363 downloads.

transfer learning with fine-tuning on custom image datasetsimagenet-21k pre-trained image classification with vision transformer architecturefeature extraction and embedding generation for downstream tasks

3 shared capabilities

Model43

nsfw_image_detector

image-classification model by undefined. 9,43,400 downloads.

vision transformer-based feature extraction for nsfw embeddingsnsfw content classification via vision transformer

2 shared capabilities

Model54

nsfw_image_detection

image-classification model by undefined. 3,40,24,086 downloads.

vision-transformer-feature-extractionbinary-nsfw-image-classification

2 shared capabilities

Model42

vit_base_patch16_224.augreg2_in21k_ft_in1k

image-classification model by undefined. 5,81,608 downloads.

feature extraction from intermediate transformer layers for representation learningvision transformer patch-based image classification with imagenet-1k fine-tuning

2 shared capabilities

Model49

nsfw-image-detection-384

image-classification model by undefined. 65,60,925 downloads.

transfer learning fine-tuning for domain-specific nsfw detectionnsfw content classification via vision transformer embeddings

2 shared capabilities

Best For

✓Content moderation teams building automated deepfake detection pipelines
✓Forensic analysts and fact-checkers verifying image authenticity
✓Social media platforms screening user-generated content at scale
✓Security researchers studying deepfake detection robustness
✓Production systems requiring safe model deserialization without code execution risks
✓Batch processing pipelines screening hundreds or thousands of images
✓Edge deployment scenarios (mobile, embedded systems) with memory constraints
✓Teams already using HuggingFace transformers ecosystem

Known Limitations

⚠Model trained on specific deepfake generation methods (likely GAN-based or face-swap tools from 2023-2024); may not generalize to novel synthesis techniques or future deepfake generators
⚠Requires 384×384 pixel input resolution; lower-resolution or heavily compressed images may degrade detection accuracy
⚠No temporal analysis — processes individual frames independently, cannot leverage video consistency cues that would improve detection in video deepfakes
⚠Unknown robustness to adversarial perturbations or intentional evasion attacks designed to fool the classifier
⚠Binary classification only (real vs. deepfake); does not identify the specific manipulation technique or provide confidence scores for borderline cases
⚠Safetensors loading adds ~50-100ms overhead on first load due to format parsing, though subsequent loads are cached

Requirements

Python 3.8+PyTorch 1.9+ or TensorFlow 2.6+ (via transformers library)transformers library 4.30+timm library 0.9+ (for model architecture)PIL/Pillow for image loading and preprocessingInput images must be in standard formats (JPEG, PNG, WebP)safetensors library 0.3+PyTorch 1.9+ or TensorFlow 2.6+

Input / Output

Accepts: image (JPEG, PNG, WebP, BMP), image tensor (3-channel RGB, normalized to [0, 1] or [-1, 1]), image file paths (list of strings), PIL Image objects (list), image tensors (batch of shape [N, 3, 384, 384]), image tensor (shape [1, 3, 384, 384]), PIL Image objects, image tensors (any device, automatically moved to model device), PIL Image objects (converted to tensors on target device), model card metadata (YAML), model weights (safetensors format), training code and hyperparameters (optional, community-provided)

Produces: classification logits (2-class: real vs. deepfake), probability scores (softmax normalized), binary label (0 = real, 1 = deepfake), classification logits (shape [N, 2]), probability scores (shape [N, 2], softmax normalized), batch predictions (list of binary labels), feature embeddings (shape [1, 384] from [CLS] token), patch embeddings (shape [1, 577, 384] for 384×384 input with 16×16 patches), attention maps (shape [num_heads, seq_len, seq_len] per layer), classification logits (on same device as model), probability scores (float32 or float16 depending on precision setting), model weights (safetensors), model card documentation, version history and commit logs

UnfragileRank

Adoption64%(40% weight)

Quality21%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

5 capabilities

Visit CommunityForensics-DeepfakeDet-ViT→

Model Details

huggingface

Provider

transformers

Architecture

757,774

Downloads

Tasks

image-classification

About

buildborderless/CommunityForensics-DeepfakeDet-ViT — a image-classification model on HuggingFace with 7,57,774 downloads

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Capabilities5 decomposed

vision transformer-based deepfake detection via patch-level feature extraction

Medium confidence

Solves for

Best for

Content moderation teams building automated deepfake detection pipelines

Forensic analysts and fact-checkers verifying image authenticity

Social media platforms screening user-generated content at scale

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+ (via transformers library)

transformers library 4.30+

Limitations

Model trained on specific deepfake generation methods (likely GAN-based or face-swap tools from 2023-2024); may not generalize to novel synthesis techniques or future deepfake generators

Requires 384×384 pixel input resolution; lower-resolution or heavily compressed images may degrade detection accuracy

No temporal analysis — processes individual frames independently, cannot leverage video consistency cues that would improve detection in video deepfakes

What makes it unique

vs alternatives

batch image classification with safetensors model loading

Medium confidence

Solves for

Best for

Production systems requiring safe model deserialization without code execution risks

Batch processing pipelines screening hundreds or thousands of images

Edge deployment scenarios (mobile, embedded systems) with memory constraints

Requires

transformers library 4.30+

safetensors library 0.3+

PyTorch 1.9+ or TensorFlow 2.6+

Limitations

Safetensors loading adds ~50-100ms overhead on first load due to format parsing, though subsequent loads are cached

Batch processing requires all images to be resized to 384×384, which may introduce artifacts for images with extreme aspect ratios

No built-in async/parallel processing — batches are processed sequentially on a single GPU/CPU

What makes it unique

vs alternatives

fine-tuned vit feature extraction for downstream forensic tasks

Medium confidence

Solves for

Best for

Researchers developing novel deepfake detection methods using transfer learning

Teams building explainable AI systems that need to visualize which image regions trigger deepfake predictions

Organizations with domain-specific deepfake datasets wanting to adapt the model with minimal retraining

Requires

PyTorch 1.9+ with hooks API support

transformers library 4.30+

timm library 0.9+

Limitations

Feature extraction requires access to intermediate model layers; not all frameworks expose these cleanly (requires custom model wrapper or hook registration)

Extracted features are 384-dimensional (ViT-Small embedding size); dimensionality reduction may be needed for efficient similarity search

Attention visualization (e.g., attention rollout) is computationally expensive for 12-layer ViT; requires ~500ms per image on CPU

What makes it unique

vs alternatives

model inference with automatic device placement and mixed-precision support

Medium confidence

Solves for

Best for

Production systems deployed across heterogeneous hardware (cloud, on-prem, edge)

Mobile or embedded applications requiring low-latency inference

Cost-sensitive deployments using cheaper GPU instances with limited VRAM

Requires

PyTorch 1.6+ (for AMP support)

CUDA 11.0+ (for GPU inference)

transformers library 4.30+

Limitations

Mixed-precision inference may introduce 0.5-2% accuracy degradation on edge cases due to float16 rounding; requires validation on target dataset

NVIDIA GPU support for mixed-precision is mature; AMD and Intel GPU support is experimental and may have compatibility issues

Automatic device placement assumes single-GPU or single-device scenarios; multi-GPU distributed inference requires manual configuration

What makes it unique

vs alternatives

community-contributed model weights with mit licensing and version tracking

Medium confidence

Solves for

Best for

Commercial teams building deepfake detection features without licensing overhead

Open-source projects requiring permissive model licensing

Researchers studying deepfake detection trends and community contributions

Requires

HuggingFace account (free) for model access and contribution

Git knowledge for contributing improvements via pull requests

Internet connection for downloading model weights from HuggingFace Hub

Limitations

Community-contributed models may lack formal validation or peer review; accuracy claims should be independently verified

Model updates are not guaranteed to be backward-compatible; version pinning is required for reproducibility

HuggingFace Model Hub availability depends on external infrastructure; no guarantee of long-term hosting

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

CommunityForensics-DeepfakeDet-ViT

Capabilities5 decomposed

vision transformer-based deepfake detection via patch-level feature extraction

batch image classification with safetensors model loading

fine-tuned vit feature extraction for downstream forensic tasks

model inference with automatic device placement and mixed-precision support

community-contributed model weights with mit licensing and version tracking

Related Artifactssharing capabilities

vit-base-patch16-224

vit-large-patch16-384

nsfw_image_detector

nsfw_image_detection

vit_base_patch16_224.augreg2_in21k_ft_in1k

nsfw-image-detection-384

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

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Data Sources

CommunityForensics-DeepfakeDet-ViT

Capabilities5 decomposed

vision transformer-based deepfake detection via patch-level feature extraction

batch image classification with safetensors model loading

fine-tuned vit feature extraction for downstream forensic tasks

model inference with automatic device placement and mixed-precision support

community-contributed model weights with mit licensing and version tracking

Related Artifactssharing capabilities

vit-base-patch16-224

vit-large-patch16-384

nsfw_image_detector

nsfw_image_detection

vit_base_patch16_224.augreg2_in21k_ft_in1k

nsfw-image-detection-384

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to CommunityForensics-DeepfakeDet-ViT

Are you the builder of CommunityForensics-DeepfakeDet-ViT?

Get the weekly brief

Data Sources