DF-Net可精准检测图像伪造，且对社交网络常见压缩操作有强鲁棒性。
DF-Net: The Digital Forensics Network for Image Forgery Detection
- 基于深度神经网络实现像素级伪造定位
- 在4个基准数据集上超越现有方法性能
- 适合应对社交媒体中的图像篡改检测
通过在线社交网络（OSN）传播的图像操纵行为，正严重威胁社会舆论。本文提出数字取证网络（DF-Net），一种用于像素级图像伪造检测的深度神经网络。所发布模型在四个主流基准数据集上表现优于多个先进方法。尤为关键的是，DF-Net对社交网络中常见的有损操作（如缩放、压缩）具有鲁棒性，能有效应对实际传播场景中的图像失真。
原文摘要 · Abstract (English)
The orchestrated manipulation of public opinion, particularly through manipulated images, often spread via online social networks (OSN), has become a serious threat to society. In this paper we introduce the Digital Forensics Net (DF-Net), a deep neural network for pixel-wise image forgery detection. The released model outperforms several state-of-the-art methods on four established benchmark datasets. Most notably, DF-Net's detection is robust against lossy image operations (e.g resizing, compression) as they are automatically performed by social networks.
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