CNN-based Multiple Manipulation Detector Using Frequency Domain Features of Image Residuals

Abstract

Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of a potent detector requires knowledge of the type of manipulation, something that cannot be known ( a priori ). Thus, the latest effort is directed towards developing model-free (i.e., generalized) detectors capable of detecting multiple manipulation types. In this article, we propose a novel detector capable of exposing seven different manipulation types in low-resolution compressed images. Our proposed approach is based on a two-layer convolutional neural network (CNN) to extract frequency domain features of image median filtered residual that are classified using two different classifiers—softmax and extremely randomized trees. Extensive experiments demonstrate the efficacy of proposed detector over existing state-of-the-art detectors.

Notice of Retraction: Perspective and Prediction of the Rule of High Temperature Melting of SiO₂ via Visual Analysis

“…CNN [32] is the main representative of the deep learning theory. This algorithm is good at extracting the structural features of digital images.…”

A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

“…To evaluate the performance of the proposed DCT-CNN on HDR source forensics, we created several datasets with different types of HDR images and different sizes of image blocks. The performance is assessed by classification accuracy (Acc), receiver operating characteristic curve (ROC) and the area under the curve (AUC), and compared with six state-of-art forensics methods in [19][20][21][22]24,27]. The classification accuracy (Acc) is defined as:…”

“…The drawbacks of this method are that some low-frequency and high-frequency DCT coefficients were discarded and the DCT coefficients are given a fixed weight, which limited the performance of this method. Singhal et al proposed a CNN-based method for detecting manipulation by converting image residuals into DCT domain [24]. The drawbacks of this method are that the DCT is conducted on the Median Filter Residual (MFR) and with no multi-scale module to extract features from different scales.…”

“…At the same time, some low-frequency DCT coefficients and high-frequency DCT coefficients in the DCT coefficient matrix are discarded. In Reference [24], the DCT transform is performed on the median filter residual of an image, which discarded the image information before the DCT operation. The method proposed in this paper uses MC-DCT to retain information in multiple color channels without discarding image information of DCT coefficients.…”

“…During this process, some features related to forensics will be lost. In Reference [24], no multi-scale feature extraction structure is proposed.…”