Summary Transformers Operating Directly on File Bytes arxiv.org
7,405 words - PDF document - View PDF document
One Line
The ByteFormer model is a transformer-based neural network that operates directly on file bytes for privacy-preserving inference on obfuscated inputs, achieving high accuracy on various input modalities without requiring modifications or hyperparameter tuning.
Key Points
- The Perceiver model includes domain-specific modeling and removes positional embedding for image classification on ImageNet.
- ByteFormer is a privacy-preserving model that operates directly on file bytes and achieves high accuracy on various input modalities.
- The choice of file encoding affects the accuracy of ByteFormer, and certain augmentations can improve accuracy.
- The model uses a transformer backbone with a configuration similar to DeiT-Ti and achieves state-of-the-art accuracy on several datasets.
- The method can be used as a building block for obfuscating inputs to a learning system and avoids the need for modality-specific preprocessing.
Summaries
166 word summary
The document discusses the development of models for operating directly on file bytes, with a focus on efficiency and accuracy. The authors compare their approach to previous works and report train time and throughput metrics. They also discuss the importance of characterizing runtime and avoiding hardware inconsistencies. The ByteFormer model is a transformer-based neural network that operates directly on file bytes for privacy-preserving inference on obfuscated inputs. It achieves high accuracy on various input modalities, including images and WAV files, without requiring modifications or hyperparameter tuning. The model maintains privacy by obfuscating input byte values using a permutation function and a custom camera that captures a privacy-preserving representation. The choice of file encoding is important when performing inference on file bytes. The ByteFormer model uses transformers with learned token embeddings and attention to handle long sequence lengths when operating directly on file bytes. The model data format includes flattened tensors in height, width, channel order for image bytes stored in HWC order without any file headers.
485 word summary
ByteFormer is a deep learning model that directly operates on file bytes for classification tasks without decoding them into modality-specific representations. It achieves high accuracy on various input modalities, including images and WAV files, without requiring modifications or hyperparameter tuning. The model maintains privacy by obfuscating input byte values using a permutation function and a custom camera that captures a privacy-preserving representation. The choice of file encoding is important when performing inference on file bytes. The ByteFormer model uses transformers with learned token embeddings and attention to handle long sequence lengths when operating directly on file bytes. The model data format includes flattened tensors in height, width, channel order for image bytes stored in HWC order without any file headers. The ByteFormer model is a transformer-based neural network that operates directly on file bytes for privacy-preserving inference on obfuscated inputs. The model can handle various input modalities without converting them into a standard input representation. The architecture of the model is ByteFormer Tiny (BF-Ti), which is based on the DeiT-Ti architecture with an embedding dimension of 192. The model is used for altering input representations and obfuscating regions of constant color in images without retraining. The optimal values for hyperparameters w and k depend on the type of file being encoded, with w = 128 and k = 32 being the best for audio files. The study compares different attention methods for Transformers operating on file bytes, finding window attention to be more effective than bag attention. The article discusses a new method called ByteFormer, which directly operates on file bytes to create vector representations of files. ByteFormer is a model that operates directly on file bytes, which can be used with image obfuscation techniques to provide security against attackers. The model's accuracy depends on the file encoding chosen, and future work includes adding invariance to file encodings and experimenting with other domains and tasks. The document references various related works and techniques, including mixup, ConvMixer, and secure computation for neural networks. The authors aim to create a model that can directly model file bytes with privacy-preserving applications.
The Perceiver model includes domain-specific modeling at inference time and removes the positional embedding. BF-Ti and DeiT are two models compared to Perceiver for image classification on ImageNet. Perceiver achieves comparable accuracy with fewer flops than BF-Ti and DeiT. Experimental results are reported for camera experiments and multi-modal modeling.
The excerpted text contains a list of references to articles and papers on various topics related to computer vision, machine learning, and cryptography. The references cover topics such as deep learning, vision transformers, object detection, and secure training with GPUs.
Overall, the document discusses the development of models for operating directly on file bytes, with a focus on efficiency and accuracy. The authors compare their approach to previous works and report train time and throughput metrics. They also discuss the importance of characterizing runtime and avoiding hardware inconsistencies.
1370 word summary
The Perceiver model includes domain-specific modeling at inference time and removes the positional embedding. BF-Ti and DeiT are two models compared to Perceiver for image classification on ImageNet. BF-Ti has a small kernel size and low fraction of retained pixels for privacy-preserving purposes. DeiT has a small patch size. Perceiver has a slower forward pass time but achieves comparable accuracy with fewer flops. The model's size and accuracy are similar to DeiT-Ti and DeiT-S, respectively. The model's training time is improved by a relatively small amount through secure featurization. Experimental results are reported for camera experiments and multi-modal modeling. This is a technical document discussing the development of models for operating directly on file bytes, with a focus on efficiency and accuracy. The authors compare their approach to previous works and report train time and throughput metrics. They also discuss the importance of characterizing runtime and avoiding hardware inconsistencies. The document references various related works and techniques, including but not limited to: mixup, ConvMixer, secure computation for neural networks, and feature interactive convolution. The authors aim to create a model that can directly model file bytes with privacy-preserving applications. The excerpted text contains a list of references to articles and papers on various topics related to computer vision, machine learning, and cryptography. The references cover topics such as deep learning, vision transformers, object detection, and secure training with GPUs. Some of the notable references include a paper on the Swin transformer, a hierarchical vision transformer using shifted windows, and an article on the discrete cosine transform. ByteFormer is a model that operates directly on file bytes, consuming only bytes and not explicitly modeling the input modality. It can be used in conjunction with image obfuscation techniques to provide security against an attacker with access to a large set of model inputs. However, the obfuscation method does not provide cryptography-level security. The accuracy of ByteFormer depends on the file encoding chosen, and adding invariance to file encodings is future work. The method has only been evaluated on classification for images and audio, and experimenting with other domains and tasks is future work. The choice of file encoding affects the accuracy of ByteFormer, and using TIFF results in a reduction of accuracy on ImageNet compared to using JPEG. Future work includes exploring fine-grained localization for detection and segmentation tasks. The article discusses a new method called ByteFormer, which directly operates on file bytes to create vector representations of files. The authors visualize learned position and token embeddings and experiment with various augmentations to understand the behavior of ByteFormer. They find that the model is sensitive to locality and byte ordering, and that certain augmentations can improve accuracy. The authors also show results for various file encodings on ImageNet and Speech Commands. The study compares different attention methods for Transformers operating on file bytes, finding window attention to be more effective than bag attention. The experiments are conducted using TIFF and JPEG images. The study also includes an analysis of the privacy-preserving camera setup, which masks pixel channels of ImageNet images at different percentages. The results show that the model is resilient to this transformation, but DeiT is not. The study provides Top-1 ImageNet accuracy of BF-Ti with different types of attention and includes illustrations of the attention methods used. Table 5 summarizes the results for the privacy-preserving camera experiment. The article presents a method for file encoding called ByteFormer Tiny (BF-Ti), which uses byte remapping to obfuscate images and audio files. The method retains shape information and achieves high accuracy in audio classification and image recognition tasks. The optimal values for hyperparameters w and k depend on the type of file being encoded, with w = 128 and k = 32 being the best for audio files. The method is computationally efficient and outperforms previous methods in some cases. Results for different noise levels and JPEG quality factors are presented, and the method is compared to related works. ByteFormer is a model that operates directly on file bytes for image classification tasks. It achieves high accuracy by reducing token lengths and using lower quality factors. The model's kernel size is reduced for JPEG images, which have a smaller token length than TIFF or PNG. ByteFormer outperforms DeiT-Ti accuracies on the ImageNet dataset and is trained with exponential moving average of weights. For Speech Commands V2, ByteFormer is trained with MixUp and other augmentations. ByteFormer is also applied to privacy-preserving camera applications. The ByteFormer model is a transformer-based neural network designed to operate directly on file bytes, allowing for privacy-preserving inference on obfuscated inputs. The model can handle a variety of input modalities without converting them into a standard input representation. To reduce the sequence length, Conv1D is used, and positional embeddings are added to the token embeddings. The model can be used for altering input representations and obfuscating regions of constant color in images without retraining. The architecture of the model is ByteFormer Tiny (BF-Ti), which is based on the DeiT-Ti architecture with an embedding dimension of 192. The kernel size is typically set to 32, and stride is always k/2. The sequence length is reduced with down-sampling layers and shifted window attention. The model can be used against an adversary depending on the threat model used and the alternative encodings available. More sophisticated methods can also be used to improve security. The ByteFormer model uses transformers with learned token embeddings and attention to handle long sequence lengths when operating directly on file bytes. The model uses strided Conv1D and shifted window attention to handle various file encodings, which can exceed 150,000 tokens. At inference time, the model does not require knowledge of the input modality and can be used for image and audio file encodings. The training method includes standard training augmentation and re-encoding files with different file encodings. The model is compared to DeiT-Ti on ImageNet top-1 accuracy using various file encodings. The model data format includes flattened tensors in height, width, channel order for image bytes stored in HWC order without any file headers. The choice of file encoding is important when performing inference on file bytes. For example, an image stored as a JPEG or PNG file will be decoded differently. MP3 encodings can be difficult to handle, but the pydub software package can help. WAV files are stored as audio signals and can be compressed using Huffman encoding. JPEG encodes images by applying a series of transformations to compress the image before serialization. PNG contains headers describing configuration options and the image size. TIFF allows for many custom configurations and can be stored in "CHW" order. The use of image input masking with a single-pixel camera is common in some works. ByteFormer is a privacy-preserving model that performs inference on file bytes without requiring image capture. The model is based on Transformers and can classify images directly from file bytes. ByteFormer achieves strong performance on a variety of image and audio file encodings, with 90% of the pixels masked during training. The model maintains privacy by obfuscating input byte values using a permutation function and a custom camera that captures a privacy-preserving representation. The method can be used as a building block for obfuscating inputs to a learning system and avoids the need for modality-specific preprocessing. ByteFormer is a model that directly operates on file bytes for classification tasks without decoding them into modality-specific representations. It uses a modified Transformer architecture and can handle various input representations, including images and audio files. The model achieves state-of-the-art accuracy on several datasets and can operate on privacy-preserving inputs to protect user privacy. The traditional practice of decoding inputs into modality-specific representations has two main drawbacks, requiring hand-crafted input representations and reducing privacy. ByteFormer is a deep learning model that operates directly on file bytes, avoiding the need to decode files at inference time. It achieves high accuracy on various input modalities, including images and WAV files, without requiring modifications or hyperparameter tuning. ByteFormer also has applications in privacy-preserving inference, as it can perform inference on obfuscated inputs. The model uses a transformer backbone with a configuration similar to DeiT-Ti and achieves an ImageNet Top-1 classification accuracy of 77.33%. The code for ByteFormer will be made available to the public.