解决社交网络深伪检测中无配对数据难题，提升压缩环境下的识别准确率。
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
- 通过细粒度与粗粒度分析聚合有无配对数据的相关性
- 在17个数据集上超越现有最优方法，显著提升压缩场景下检测性能
- 适合关注真实社交平台深伪检测的工程师与安全研究者
尽管深度伪造检测技术取得进展，但在线社交网络（OSN）中因不同压缩导致的图像质量差异仍是挑战。现有方法依赖成对图像（原始与压缩）间的相关性，但在开放世界场景中，配对数据稀缺，仅压缩图像易得而原始图像难寻，造成无配对数据远多于配对数据，导致检测性能下降。为此，提出开放世界深伪检测网络（ODDN），包含两个核心模块：开放世界数据聚合（ODA）与压缩丢弃梯度修正（CGC）。ODA通过细粒度分析配对数据、粗粒度分析无配对数据，有效聚合压缩与原始样本间的关系；CGC引入压缩丢弃梯度修正机制，优化训练梯度，使模型对各类压缩方式保持鲁棒性。在17个主流深伪数据集上的大量实验表明，ODDN优于当前最先进方法。
原文摘要 · Abstract (English)
Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging. Current methods succeed by leveraging correlations between paired images, whether raw or compressed. However, in open-world scenarios, paired data is scarce, with compressed images readily available but corresponding raw versions difficult to obtain. This imbalance, where unpaired data vastly outnumbers paired data, often leads to reduced detection performance, as existing methods struggle without corresponding raw images. To overcome this issue, we propose a novel approach named the open-world deepfake detection network (ODDN), which comprises two core modules: 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 for paired and unpaired data, respectively. CGC incorporates a compression-discard gradient correction to further enhance performance across diverse compression methods in OSN. This technique optimizes the training gradient to ensure the model remains insensitive to compression variations. Extensive experiments conducted on 17 popular deepfake datasets demonstrate the superiority of the ODDN over SOTA baselines.
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