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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.","intents":["Detect whether a face image is a deepfake or authentic for content moderation systems","Screen user-uploaded profile pictures or video frames for synthetic faces in social platforms","Identify manipulated facial imagery in forensic investigations or fact-checking workflows","Build 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","Security researchers studying deepfake detection robustness"],"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"],"requires":["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 preprocessing","Input images must be in standard formats (JPEG, PNG, WebP)"],"input_types":["image (JPEG, PNG, WebP, BMP)","image tensor (3-channel RGB, normalized to [0, 1] or [-1, 1])"],"output_types":["classification logits (2-class: real vs. deepfake)","probability scores (softmax normalized)","binary label (0 = real, 1 = deepfake)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-buildborderless--communityforensics-deepfakedet-vit__cap_1","uri":"capability://data.processing.analysis.batch.image.classification.with.safetensors.model.loading","name":"batch image classification with safetensors model loading","description":"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.","intents":["Load the deepfake detection model safely without pickle deserialization vulnerabilities","Process multiple images in a single inference pass for throughput optimization","Deploy the model on edge devices or low-memory environments using memory-mapped weights","Integrate 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","Teams already using HuggingFace transformers ecosystem"],"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","Memory usage scales linearly with batch size; large batches on limited-memory devices may cause OOM errors"],"requires":["transformers library 4.30+","safetensors library 0.3+","PyTorch 1.9+ or TensorFlow 2.6+","HuggingFace model card access (internet connection for first download, or pre-cached model)"],"input_types":["image file paths (list of strings)","PIL Image objects (list)","image tensors (batch of shape [N, 3, 384, 384])"],"output_types":["classification logits (shape [N, 2])","probability scores (shape [N, 2], softmax normalized)","batch predictions (list of binary labels)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-buildborderless--communityforensics-deepfakedet-vit__cap_2","uri":"capability://data.processing.analysis.fine.tuned.vit.feature.extraction.for.downstream.forensic.tasks","name":"fine-tuned vit feature extraction for downstream forensic tasks","description":"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.","intents":["Extract forensic feature embeddings from face images for similarity-based deepfake detection or clustering","Use learned representations as input to custom classifiers trained on domain-specific deepfake datasets","Analyze which image regions or patches contribute most to deepfake predictions via attention visualization","Build 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","Forensic analysts needing to cluster or search for similar manipulated images"],"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","Transfer learning performance depends heavily on domain similarity between training data and target deepfake dataset; may require 100+ labeled examples for effective fine-tuning"],"requires":["PyTorch 1.9+ with hooks API support","transformers library 4.30+","timm library 0.9+","Optional: matplotlib or similar for attention visualization","Optional: scikit-learn or faiss for feature clustering/search"],"input_types":["image tensor (shape [1, 3, 384, 384])","PIL Image objects"],"output_types":["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)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-buildborderless--communityforensics-deepfakedet-vit__cap_3","uri":"capability://automation.workflow.model.inference.with.automatic.device.placement.and.mixed.precision.support","name":"model inference with automatic device placement and mixed-precision support","description":"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.","intents":["Deploy the deepfake detector on diverse hardware (cloud GPUs, edge devices, CPU-only servers) without manual device configuration","Reduce inference latency and memory usage on resource-constrained devices via mixed-precision inference","Scale batch processing on limited-memory GPUs by enabling lower-precision weights","Ensure 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","Teams without ML infrastructure expertise needing automatic hardware optimization"],"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","Mixed-precision inference requires CUDA Compute Capability 7.0+ (V100, A100, RTX series); older GPUs fall back to float32"],"requires":["PyTorch 1.6+ (for AMP support)","CUDA 11.0+ (for GPU inference)","transformers library 4.30+","Optional: NVIDIA Apex library for advanced mixed-precision features"],"input_types":["image tensors (any device, automatically moved to model device)","PIL Image objects (converted to tensors on target device)"],"output_types":["classification logits (on same device as model)","probability scores (float32 or float16 depending on precision setting)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-buildborderless--communityforensics-deepfakedet-vit__cap_4","uri":"capability://memory.knowledge.community.contributed.model.weights.with.mit.licensing.and.version.tracking","name":"community-contributed model weights with mit licensing and version tracking","description":"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.","intents":["Use a community-vetted deepfake detection model in commercial products without licensing concerns","Contribute improvements or alternative training approaches to the model via pull requests","Reproduce the model's training and evaluation using published methodology and hyperparameters","Track 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","Organizations wanting to audit model provenance and training methodology"],"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","MIT license permits commercial use but provides no warranty or liability protection; users assume all risk"],"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"],"input_types":["model card metadata (YAML)","model weights (safetensors format)","training code and hyperparameters (optional, community-provided)"],"output_types":["model weights (safetensors)","model card documentation","version history and commit logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"low","permissions":["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 preprocessing","Input images must be in standard formats (JPEG, PNG, WebP)","safetensors library 0.3+","PyTorch 1.9+ or TensorFlow 2.6+","HuggingFace model card access (internet connection for first download, or pre-cached model)","PyTorch 1.9+ with hooks API support"],"failure_modes":["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","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","Memory usage scales linearly with batch size; large batches on limited-memory devices may cause OOM errors","Feature extraction requires access to intermediate model layers; not all frameworks expose these cleanly (requires custom model wrapper or hook registration)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6429797420759115,"quality":0.35,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:59.355Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":793976,"model_likes":12}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=buildborderless--communityforensics-deepfakedet-vit","compare_url":"https://unfragile.ai/compare?artifact=buildborderless--communityforensics-deepfakedet-vit"}},"signature":"rFPMrS2MRvVGXyYPsIabNjcij9mNPP6mUM0ujV/Sv9/0xOPfZ2z46Ofchls0MYNoDzp7S24NFtwaVraXFSOqBw==","signedAt":"2026-06-21T06:24:56.393Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/buildborderless--communityforensics-deepfakedet-vit","artifact":"https://unfragile.ai/buildborderless--communityforensics-deepfakedet-vit","verify":"https://unfragile.ai/api/v1/verify?slug=buildborderless--communityforensics-deepfakedet-vit","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}