Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings

• URL: http://arxiv.org/abs/2401.15048v2
• Date: Wed, 28 Aug 2024 09:42:44 GMT
• Title: Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings
• Authors: Dmytro Zakharov, Oleksandr Kuznetsov, Emanuele Frontoni,
• Abstract summary: We introduce an innovative image transformation technique that renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. The proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. We show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy.
• Score: 22.338328674283062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biometric authentication systems play a crucial role in modern security systems. However, maintaining the balance of privacy and integrity of stored biometrics derivative data while achieving high recognition accuracy is often challenging. Addressing this issue, we introduce an innovative image transformation technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models, which allows the distorted photo version to be stored for further verification. While initially intended for biometrics systems, the proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. By experimenting with widely used datasets LFW and MNIST, we show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy. We compare our method with previously state-of-the-art approaches. We publically release the source code.

Related papers

• Synthetic Forehead-creases Biometric Generation for Reliable User Verification [6.639785884921617]
  We present a new framework to synthesize forehead-crease image data while maintaining important features, such as uniqueness and realism. We evaluate the diversity and realism of the generated forehead-crease images using the Fr'echet Inception Distance (FID) and the Structural Similarity Index Measure (SSIM)
  arXiv  Detail & Related papers  (2024-08-28T10:33:00Z)
• Embedding Non-Distortive Cancelable Face Template Generation [22.80706131626207]
  We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. We test the reliability of biometric recognition networks by determining the maximum image distortion that does not change the predicted identity.
  arXiv  Detail & Related papers  (2024-02-04T15:39:18Z)
• TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks [7.092869001331781]
  Face recognition systems are widely deployed in high-security applications. Digital manipulations, such as face morphing, pose a security threat to face recognition systems. We present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks.
  arXiv  Detail & Related papers  (2024-01-21T21:04:05Z)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• FACE-AUDITOR: Data Auditing in Facial Recognition Systems [24.082527732931677]
  Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them. We propose a complete toolkit FACE-AUDITOR that can query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model.
  arXiv  Detail & Related papers  (2023-04-05T23:03:54Z)
• Disguise without Disruption: Utility-Preserving Face De-Identification [40.484745636190034]
  We introduce Disguise, a novel algorithm that seamlessly de-identifies facial images while ensuring the usability of the modified data. Our method involves extracting and substituting depicted identities with synthetic ones, generated using variational mechanisms to maximize obfuscation and non-invertibility. We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
  arXiv  Detail & Related papers  (2023-03-23T13:50:46Z)
• Attribute-preserving Face Dataset Anonymization via Latent Code Optimization [64.4569739006591]
  We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN. We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
  arXiv  Detail & Related papers  (2023-03-20T17:34:05Z)
• Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis [69.09526348527203]
  Deep generative models have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. We propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios.
  arXiv  Detail & Related papers  (2021-05-29T21:22:24Z)
• HM4: Hidden Markov Model with Memory Management for Visual Place Recognition [54.051025148533554]
  We develop a Hidden Markov Model approach for visual place recognition in autonomous driving. Our algorithm, dubbed HM$4$, exploits temporal look-ahead to transfer promising candidate images between passive storage and active memory. We show that this allows constant time and space inference for a fixed coverage area.
  arXiv  Detail & Related papers  (2020-11-01T08:49:24Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)
• Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
  We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image. We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
  arXiv  Detail & Related papers  (2020-01-12T05:26:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.