This paper presents a new segmentation algorithm and deep learning concept for innovative copy-move image forgery identification. In this work, the segmentation algorithm uses a new Adaptive Harris Hawk Optimization (AHHO) algorithm. The host image is segmented into irregular and non-overlapping blocks, named as Image Blocks (IB). Here the segmented image is compressed with a Discrete Cosine Transform (DCT). After that, the Zernike moments and Gabor filter-based features extraction techniques are processed to extract the Block Features (BF). At last, the tampered portions are classified with the hybrid Deep Neural Network (DNN) and Flower Pollination Algorithm (FPA) for forgery classification. This novel deep learning algorithm reduces the learning complexities and improves detection accuracy. The compression attacks are removed with anti-forensic blocking artefact removal concept. The experiments are conducted on the Benchmark dataset, CoMoFoD, and GRIP. For the benchmark dataset, precision, recall and F1 values are 91.27,100.0 and 96.07, respectively. For CoMoFoD dataset, the values of precision, recall and F1 are 92.57, 98.0 and 93.05, respectively. For GRIP dataset, the values of precision, recall and F1 are 97.02, 98.0 and 93.05, respectively. The implementation outcomes demonstrated that the proposed scheme is most effective in copy-move forgery recognition than the existing forgery detection approaches.

本文提出了一种新的分割算法和深度学习概念，用于创新的复制移动图像伪造识别。在这项工作中，分割算法使用了新的自适应哈里斯霍克优化（AHHO）算法。主机图像被分为不规则和不重叠的块，称为图像块（IB）。在这里，分割图像通过离散余弦变换（DCT）进行压缩。之后，处理Zernike矩和基于Gabor滤波器的特征提取技术，以提取块特征（BF）。最后，利用混合深度神经网络（DNN）和花授粉算法（FPA）对被篡改的部分进行伪造分类。这种新颖的深度学习算法可降低学习复杂度并提高检测精度。压缩攻击通过反法医伪影消除概念消除。实验是在Benchmark数据集，CoMoFoD和GRIP上进行的。对于基准数据集，精度，召回率和F1值分别为91.27、100.0和96.07。对于CoMoFoD数据集，精度，召回率和F1的值分别为92.57、98.0和93.05。对于GRIP数据集，精度，召回率和F1的值分别为97.02、98.0和93.05。实施结果表明，与现有的伪造检测方法相比，该方案在复制移动伪造识别中最有效。Precision，Recall和F1的值分别为92.57、98.0和93.05。对于GRIP数据集，精度，召回率和F1的值分别为97.02、98.0和93.05。实施结果表明，与现有的伪造检测方法相比，该方案在复制移动伪造识别中最有效。Precision，Recall和F1的值分别为92.57、98.0和93.05。对于GRIP数据集，精度，召回率和F1的值分别为97.02、98.0和93.05。实施结果表明，与现有的伪造检测方法相比，该方案在复制移动伪造识别中最有效。