access icon free Image copy-move forgery detection algorithm based on ORB and novel similarity metric

© The Institution of Engineering and Technology
Received 29/08/2019, Accepted 02/03/2020, Revised 04/02/2020, Published 10/03/2020

Image forgery poses a serious threat in electric power, medicine and other fields. Relevant departments need to pay a great price to identify the authenticity of the image. For traditional copy-move forgery image detection, the existing methods have at least two problems: low robustness and poor matching caused by a low number of feature points. Here, a novel similarity metric combining cosine and Jaccard is proposed to improve feature matching, which combines with oriented features from accelerated segment test and rotated binary robust independent elementary features (ORB) feature extraction to realise effective and fast image forgery detection. First, the image is divided into overlapping blocks, and ORB is used to extract the feature points of each image block to obtain the text information. Second, the novel similarity metric is used to calculate similarity and match the text. Finally, two image blocks with the highest similarity are located. The experimental results show that, on the one hand, ORB can greatly lessen detection time. On the other hand, the novel similarity metric can improve the poor matching caused by the small number of feature points. Combining the two methods can exhibit high robustness to translation, rotation, noise, illumination and JPEG compression.

Inspec keywords: image forensics; image segmentation; image matching; feature extraction

Other keywords: image authenticity; rotated binary robust independent elementary features feature extraction; novel similarity metric combining cosine; ORB feature extraction; JPEG compression; copy-move forgery image detection; accelerated segment testing; feature matching; Jaccard; image copy-move forgery detection algorithm

Subjects: Image recognition; Computer vision and image processing techniques

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