Blind authentication is one of the challenging techniques which has attracted considerable attention with the increasing security hacks on digital images. This paper evaluates different models based on feature extraction to detect digitally-altered images. Various feature-extraction methods have been investigated and compared including LBP, MSLBP, CSLBP, SLBP, WLD, LPQ, MSLPQ and DCT. Moreover, we propose a number of ways to combine a variety of features utilizing single and multi-scale representations of images. For classification, SVM is employed with different kernels to identify forged and authentic images. To evaluate the effectiveness of the investigated models, several experiments have been conducted using $k$-fold cross-validation and computed performance measures for two benchmark image tampering datasets (CASIA v1.0 and CASIA v2.0). Additionally, we have conducted statistical analysis for the top-ten models and the results confirmed that the best models for CASIA v1.0 and CASIA v2.0 are MSLBP-DCT and MSLPQ, respectively. Further improvements have been achieved by integrating features from the three color channels (Y, Cb and Cr) with and without feature reduction using PCA and LLP. In this case, the results show that MSLPQ-DCT achieved better accuracy of 98.56% on CASIA v1.0 with 1020 features and MSLPQ achieved a slightly better accuracy of 97.4% on CASIA v2.0 with 1536 features.

盲认证是一项具有挑战性的技术，随着对数字图像的安全性攻击日益增加，它已引起了相当大的关注。本文基于特征提取来评估数字模型，以评估不同的模型。已经研究和比较了各种特征提取方法，包括LBP，MSLBP，CSLBP，SLBP，WLD，LPQ，MSLPQ和DCT。此外，我们提出了多种方法来利用图像的单尺度和多尺度表示来组合各种特征。为了进行分类，将SVM与不同的内核一起使用以识别伪造的图像和真实的图像。为了评估所研究模型的有效性，已使用以下方法进行了一些实验$ķ$两个基准图像篡改数据集（CASIA v1.0和CASIA v2.0）的多重交叉验证和计算的性能指标。此外，我们对前十种模型进行了统计分析，结果证实CASIA v1.0和CASIA v2.0的最佳模型分别是MSLBP-DCT和MSLPQ。通过集成来自三个颜色通道（Y，Cb和Cr）的特征（使用和不使用PCA和LLP进行特征缩减），可以实现进一步的改进。在这种情况下，结果表明，MSLPQ-DCT在具有1020个功能的CASIA v1.0上实现了98.56％的更好精度，而MSLPQ在具有1536个功能的CASIA v2.0上实现了97.4％的更好精度。