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A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image.

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Title: A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image.
Authors: Mohammed Ali Alhaidery, Manaf1 manaf.m@uokerbala.edu.iq, Abdulkadhim Jabbar Al Ali, Israa1, Jumaah Ahmed Alqays, Noura1
Source: Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p281-288. 8p.
Subjects: Digital forensics, Photographic editing
Abstract (English): With the development of cyber security and multimedia forensics, digital image manipulation has recently been recognized as one of the major challenges in forensic image analysis. Therefore, selecting an image area and then copying and pasting it into the same image is the hardest process in passive image forgery. This act violates privacy and secrecy of authenticity of digital image. The attacker exploits the available tools of editing image program to make the fake image similar to the original one. This paper presents a proposed fast and efficient passive Copy-move forgery detection scheme. Hessian- Affine and Harris-Affine detectors, and Shift Invariant Feature Transform (SIFT) descriptor, are employed in the proposed scheme. These detectors provide sufficient key points for detecting the duplicated regions in the case of small or invisible regions. The experimental results show that the proposed scheme is invariant against simple and hard attacks like uniform or non-uniform transformation. The proposed scheme was evaluated using standard data sets (GRIP, MICC 220, and F8 Multi). Resulted True Positive Rate (TPR) was 0.98 and False Positive Rate (FPR) was 0.035. Thus, the scheme is effective and providing valuable results compared to recent passive image authentication schemes. [ABSTRACT FROM AUTHOR]
Abstract (Arabic): تركز المقالة على نظام قوي للتحليل الجنائي السلبي للصور مصمم لاكتشاف التزوير بنسخ وتحريك (copy-move forgery) في الصور الرقمية. يقترح النظام نهجًا هجينًا متعدد المقاييس يجمع بين كاشفي هيسيان-أفاين (Hessian-Affine) وهاريس-أفاين (Harris-Affine) مع واصف تحويل الميزات غير المتغير بالمقياس (SIFT) لتحديد المناطق المكررة، بما في ذلك المناطق الصغيرة، غير المرئية، والمتحولة هندسيًا. يستخدم النظام ترشيح توافق العينة بأقصى احتمال (Maximum Likelihood Sample Consensus - MLESAC) لتقليل الإيجابيات الكاذبة، ويُظهر فعالية عالية وثباتًا ضد التحولات الفوتومترية، والتحولات الموحدة وغير الموحدة (الأفاين). تم تقييم الطريقة على مجموعات بيانات معيارية مثل GRIP وMICC-F600 وMICC-F8 Multi، حيث حققت معدل إيجابيات حقيقي (True Positive Rate) بنسبة 98.8% ومعدل إيجابيات كاذبة (False Positive Rate) بنسبة 3.5%، متفوقة على عدة تقنيات حديثة لاكتشاف تزوير النسخ والتحريك. يهدف العمل المستقبلي إلى دمج طرق التعلم الآلي والتعلم العميق، مثل الشبكات العصبية الالتفافية (convolutional neural networks)، لتصنيف المناطق الأصلية والمزورة بشكل أدق. [Extracted from the article]
Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Label: Title
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  Data: A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Mohammed+Ali+Alhaidery%2C+Manaf%22">Mohammed Ali Alhaidery, Manaf</searchLink><relatesTo>1</relatesTo><i> manaf.m@uokerbala.edu.iq</i><br /><searchLink fieldCode="AR" term="%22Abdulkadhim+Jabbar+Al+Ali%2C+Israa%22">Abdulkadhim Jabbar Al Ali, Israa</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Jumaah+Ahmed+Alqays%2C+Noura%22">Jumaah Ahmed Alqays, Noura</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Iraqi+Journal+for+Electrical+%26+Electronic+Engineering%22">Iraqi Journal for Electrical & Electronic Engineering</searchLink>. Jun2026, Vol. 22 Issue 1, p281-288. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+forensics%22">Digital forensics</searchLink><br /><searchLink fieldCode="DE" term="%22Photographic+editing%22">Photographic editing</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: With the development of cyber security and multimedia forensics, digital image manipulation has recently been recognized as one of the major challenges in forensic image analysis. Therefore, selecting an image area and then copying and pasting it into the same image is the hardest process in passive image forgery. This act violates privacy and secrecy of authenticity of digital image. The attacker exploits the available tools of editing image program to make the fake image similar to the original one. This paper presents a proposed fast and efficient passive Copy-move forgery detection scheme. Hessian- Affine and Harris-Affine detectors, and Shift Invariant Feature Transform (SIFT) descriptor, are employed in the proposed scheme. These detectors provide sufficient key points for detecting the duplicated regions in the case of small or invisible regions. The experimental results show that the proposed scheme is invariant against simple and hard attacks like uniform or non-uniform transformation. The proposed scheme was evaluated using standard data sets (GRIP, MICC 220, and F8 Multi). Resulted True Positive Rate (TPR) was 0.98 and False Positive Rate (FPR) was 0.035. Thus, the scheme is effective and providing valuable results compared to recent passive image authentication schemes. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Arabic)
  Group: Ab
  Data: تركز المقالة على نظام قوي للتحليل الجنائي السلبي للصور مصمم لاكتشاف التزوير بنسخ وتحريك (copy-move forgery) في الصور الرقمية. يقترح النظام نهجًا هجينًا متعدد المقاييس يجمع بين كاشفي هيسيان-أفاين (Hessian-Affine) وهاريس-أفاين (Harris-Affine) مع واصف تحويل الميزات غير المتغير بالمقياس (SIFT) لتحديد المناطق المكررة، بما في ذلك المناطق الصغيرة، غير المرئية، والمتحولة هندسيًا. يستخدم النظام ترشيح توافق العينة بأقصى احتمال (Maximum Likelihood Sample Consensus - MLESAC) لتقليل الإيجابيات الكاذبة، ويُظهر فعالية عالية وثباتًا ضد التحولات الفوتومترية، والتحولات الموحدة وغير الموحدة (الأفاين). تم تقييم الطريقة على مجموعات بيانات معيارية مثل GRIP وMICC-F600 وMICC-F8 Multi، حيث حققت معدل إيجابيات حقيقي (True Positive Rate) بنسبة 98.8% ومعدل إيجابيات كاذبة (False Positive Rate) بنسبة 3.5%، متفوقة على عدة تقنيات حديثة لاكتشاف تزوير النسخ والتحريك. يهدف العمل المستقبلي إلى دمج طرق التعلم الآلي والتعلم العميق، مثل الشبكات العصبية الالتفافية (convolutional neural networks)، لتصنيف المناطق الأصلية والمزورة بشكل أدق. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.37917/ijeee.22.1.26
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 281
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      – SubjectFull: Digital forensics
        Type: general
      – SubjectFull: Photographic editing
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    Titles:
      – TitleFull: A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image.
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            NameFull: Abdulkadhim Jabbar Al Ali, Israa
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              M: 06
              Text: Jun2026
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