Anti-Compression Contrastive Facial Forgery Detection

• URL: http://arxiv.org/abs/2302.06183v1
• Date: Mon, 13 Feb 2023 08:34:28 GMT
• Title: Anti-Compression Contrastive Facial Forgery Detection
• Authors: Jiajun Huang, Xinqi Zhu, Chengbin Du, Siqi Ma, Surya Nepal, Chang Xu
• Abstract summary: We propose an anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting the clean data.
• Score: 38.69677442287986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection model that can handle the data compressed with unknown levels is important. To enhance the performance for such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, the algorithm measures the pair-wise similarity within data as the relations, and forcing the relations of weak and strong compressed data close to each other, thus improving the discriminate power for detecting strong compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting the clean data.

Related papers

• ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks [51.03118447290247]
  We propose the open-world deepfake detection network (ODDN), which comprises open-world data aggregation (ODA) and compression-discard gradient correction (CGC) ODA effectively aggregates correlations between compressed and raw samples through both fine-grained and coarse-grained analyses. CGC incorporates a compression-discard gradient correction to further enhance performance across diverse compression methods in online social networks (OSNs)
  arXiv  Detail & Related papers  (2024-10-24T12:32:22Z)
• Lightweight Correlation-Aware Table Compression [58.50312417249682]
  $texttVirtual$ is a framework that integrates seamlessly with existing open formats. Experiments on data-gov datasets show that $texttVirtual$ reduces file sizes by up to 40% compared to Apache Parquet.
  arXiv  Detail & Related papers  (2024-10-17T22:28:07Z)
• Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth [83.15263499262824]
  We prove that gradient descent converges to a solution that completely disregards the sparse structure of the input. We show how to improve upon Gaussian performance for the compression of sparse data by adding a denoising function to a shallow architecture. We validate our findings on image datasets, such as CIFAR-10 and MNIST.
  arXiv  Detail & Related papers  (2024-02-07T16:32:29Z)
• Neural-based Compression Scheme for Solar Image Data [8.374518151411612]
  We propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images.
  arXiv  Detail & Related papers  (2023-11-06T04:13:58Z)
• Streaming Compression of Scientific Data via weak-SINDy [0.0]
  A streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. We propose modifications to the streaming weak-SINDy algorithm to accommodate the dynamically updated POD basis. By combining the built model from the streaming weak-SINDy algorithm and a small amount of data samples, the full data flow could be reconstructed accurately at a low memory cost.
  arXiv  Detail & Related papers  (2023-08-29T01:29:26Z)
• Using Convolutional Neural Networks to Detect Compression Algorithms [0.0]
  We use a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.
  arXiv  Detail & Related papers  (2021-11-17T11:03:16Z)
• Metric Learning for Anti-Compression Facial Forgery Detection [32.33501564446107]
  We propose a novel anti-compression facial forgery detection framework. It learns a compression-insensitive embedding feature space utilizing both original and compressed forgeries.
  arXiv  Detail & Related papers  (2021-03-15T14:11:14Z)
• Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach [78.05383266222285]
  It is still an open challenge to achieve the goal of data hiding that can be against these compressions. Deep learning has shown large success in data hiding, while non-differentiability of JPEG makes it challenging to train a deep pipeline for improving robustness against lossy compression. In this work, we propose a simple yet effective approach to address all the above limitations at once.
  arXiv  Detail & Related papers  (2020-12-30T12:30:09Z)
• Analyzing and Mitigating JPEG Compression Defects in Deep Learning [69.04777875711646]
  We present a unified study of the effects of JPEG compression on a range of common tasks and datasets. We show that there is a significant penalty on common performance metrics for high compression.
  arXiv  Detail & Related papers  (2020-11-17T20:32:57Z)

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.