Images play an important role in transmitting visual information in our life. It could lead to severe consequences if images are manipulated or tampered maliciously. Digital forensics is an important research area to secure multimedia information. Many forensics technologies are applied to protect our community from the abuse of digital information. In many cases, after tampering, attackers could apply operations such as resampling, JPEG compression, blurring, etc. to cover the traces of tampering. Therefore, it is necessary to detect these manipulations in image forensics before exposing forgeries. In this paper, we propose to employ the prediction error filters, ConvNeXt blocks and convolution modules to classify images with different compression quality factors and resampling rates. By tracing the inconsistencies of resampling rates and compression quality factors, it could provide supplementary information for forensics researchers to expose possible forgeries. The proposed method could achieve great classification performance regardless of the interpolation algorithms. Also, it is highly robust against JPEG compression. In addition, the proposed method can be applied for estimating quality factors of JPEG compression.

图像在我们的生活中起着传递视觉信息的重要作用。如果图像被恶意操纵或篡改，可能会导致严重后果。数字取证是保护多媒体信息安全的重要研究领域。许多取证技术被用于保护我们的社区免受数字信息的滥用。在许多情况下，篡改后，攻击者可以应用重采样、JPEG 压缩、模糊等操作来掩盖篡改痕迹。因此，有必要在揭露伪造之前检测图像取证中的这些操纵。在本文中，我们建议使用预测误差滤波器、ConvNeXt 块和卷积模块对具有不同压缩质量因子和重采样率的图像进行分类。通过追踪重采样率和压缩质量因素的不一致性，它可以为取证研究人员提供补充信息，以揭露可能的伪造行为。无论插值算法如何，所提出的方法都可以实现良好的分类性能。此外，它对 JPEG 压缩非常稳健。此外，所提出的方法可用于估计 JPEG 压缩的质量因素。