{"id":1465,"date":"2016-09-21T14:14:37","date_gmt":"2016-09-21T14:14:37","guid":{"rendered":"https:\/\/www.digitalforensicscorp.com\/blog\/?p=1465"},"modified":"2024-12-11T18:55:49","modified_gmt":"2024-12-11T18:55:49","slug":"blurring-detection-using-ela","status":"publish","type":"post","link":"https:\/\/www.digitalforensics.com\/blog\/software\/blurring-detection-using-ela\/","title":{"rendered":"ELA in Digital Forensics: Identifying Modified Image Regions"},"content":{"rendered":"

The field of digital image forensics<\/a> relies on a variety of specialized techniques to uncover hidden manipulations in visual data. In our previous article we discussed using ELA for detection of fragments insertion from the same image or another image. This method also allows examiner to detect other image modifications. For example, image scaling, brightness, contrast and saturation correction, editing with an image editor, and filters usage.<\/p>\n

But ELA results interpretation in such cases demands more experience from examiner. The best way to gain it is analysing images with known modifications.<\/p>\n

For example, let’s examine blurred background on an image. On figures 1 and 2 you can see the image and its error pattern, on figures 3 and 4 – Gaussian Blur filter usage results.<\/p>\n

\"1\"<\/p>\n

Figure 1. Original image<\/em><\/p>\n

\"2\"<\/p>\n

Figure 2. Image error pattern<\/em><\/p>\n

\"3\"<\/p>\n

Figure 3. Gaussian Blur filter<\/em><\/p>\n

\"4\"<\/p>\n

Figure 4. Image error pattern<\/em><\/p>\n

As you can see, error pattern on the first picture is quite\u00a0homogeneous. Only sky on the pattern has dark colour. Error pattern of compressed images always has dark parts only if the image has homogeneous, without small parts, background.<\/p>\n

On modified picture you can clearly see parts with different error pattern. One part is for the monument on the front, the other – for blurred background.<\/p>\n

To understand it better, let’s examine an image, where background blurring became the result of using wide aperture (fig. 5, 6).<\/p>\n

\"5\"<\/p>\n

Figure 5. Background blurring with wide aperture<\/em><\/p>\n

\"6\"<\/p>\n

Figure 6. Image error pattern<\/em><\/p>\n

Error pattern of such image shows smooth error level changing from front to background. This change corresponds with image sharpness changes because of disposal of focus point. It’s difficult to find smooth changes on original images where background objects are beyond the depth of field. But you will never find such changes in images modified with editors if a mask was used for it.<\/p>\n

Sometimes it’s very difficult to interpret ELA results right while examining blurring. The image on figure 7 illustrates an article about making photos with shallow depth of field. But on its error pattern you can clearly see two zones: the first (a tree and a squirrel) resembles sharpness improving results, the other (background) – resembles blurring results.<\/p>\n

\"7\"<\/p>\n

Figure 7. Original image<\/em><\/p>\n

\"8\"<\/p>\n

Figure 8. Image error pattern<\/em><\/p>\n

 <\/p>\n\n\n

<\/span>Error Level Analysis<\/span><\/h2>\n\n\n\n

Nowadays Error Level Analysis or ELA has become the most common method for image modification detection. This method is used by lots of both commercial and free tools and web-services [1, 2].\u00a0<\/p>\n\n\n\n

This method is based on the fact that JPEG image compression removes overmuch information about original\u2019s brightness and color [3]. The amount of information being removed depends on compression rate and, of course, signal quantization matrix. If original image compressed only once is quantizated by the same matrix as the original, modified image won\u2019t be different from the original. But if the original image is saved in JPEG a number of times, there will be significant differences between the original and modified image [4]. The result of ELA is error pattern, which shows differences between the original and modified image. The aim of the forensic examiner is to interpret error pattern right.<\/p>\n\n\n\n

This method works very well for modification detection in images created from fragments with different error level (fig. 9, 10).<\/p>\n\n\n

\n
\"ela1\"<\/figure>\n<\/div>\n\n\n

Figures 9,10. Modified image and its error pattern<\/em><\/p>\n\n\n\n

But if fragments are taken from files with the same error level or are cloned from the same image, such analysis won\u2019t bring good results, especially when your tool doesn\u2019t allow to choose compression rate value for calculating error pattern (fig. 11, 12, 13).<\/p>\n\n\n

\n
\"ela2\"<\/figure>\n<\/div>\n\n\n

Figure 11. An image compressed with 85 quality created from fragments of an image with 50 quality<\/em><\/p>\n\n\n

\n
\"ela3\"<\/figure>\n<\/div>\n\n\n

Figure 12. Error pattern calculated for 85 quality<\/em><\/p>\n\n\n

\n
\"ela4\"<\/figure>\n<\/div>\n\n\n

Figure 13. Error pattern calculated for 50 quality. Now we can see cloned fragments<\/em><\/p>\n\n\n\n

The chances of successful modification detection depend on technique of editing used. For example, if fragment was pasted in a cropped image which wasn\u2019t saved in between, reliability of the method decreases. The same can be said about highly compressed images.<\/p>\n\n\n\n

<\/span>About the authors:<\/span><\/h2>\n\n\n\n

Serge Petrov<\/p>\n\n\n\n

Interests: Digital Video Forensics, Forgery Detection, Audio Forensics<\/p>\n\n\n\n

Igor Mikhaylov<\/a><\/p>\n\n\n\n

Interests: Computer, Cell Phone & Chip-Off Forensics<\/p>\n\n\n\n

Oleg Skulkin<\/a><\/p>\n\n\n\n

Interests: iOS forensics, Android forensics, Mac OS X forensics, Windows forensics, Linux forensics<\/p>\n\n\n\n

<\/span>References:<\/span><\/h2>\n\n\n\n
    \n
  1. AMPED software. Authenticate.\u00a0https:\/\/ampedsoftware.com\/authenticate<\/a><\/li>\n\n\n\n
  2. Fotoforensics.\u00a0http:\/\/fotoforensics.com\/<\/a><\/li>\n\n\n\n
  3. Joint Photographic Experts Group.\u00a0https:\/\/jpeg.org<\/a><\/li>\n\n\n\n
  4. A Picture\u2019s Worth: Digital Image Analysis and Forensics. Dr. Neal Krawetz, Hacker Factor Solutions, August 2007. Presented at the Black Hat Briefings 2007.\u00a0https:\/\/www.blackhat.com\/presentations\/bh-dc-08\/Krawetz\/Whitepaper\/bh-dc-08-krawetz-WP.pdf<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"The field of digital image forensics relies on a variety of specialized techniques to uncover hidden manipulations in…","protected":false},"author":126,"featured_media":1474,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"csco_display_header_overlay":false,"csco_singular_sidebar":"","csco_page_header_type":"","footnotes":""},"categories":[6],"tags":[],"class_list":{"0":"post-1465","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-software","8":"cs-entry"},"yoast_head":"\nELA Forensics: Detecting Blurring and Image Manipulation<\/title>\n<meta name=\"description\" content=\"Learn how Error Level Analysis (ELA) helps digital forensic 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Sobiecki","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.digitalforensics.com\/blog\/wp-content\/uploads\/2025\/07\/viktor-sobiecki_avatar-96x96.jpg","url":"https:\/\/www.digitalforensics.com\/blog\/wp-content\/uploads\/2025\/07\/viktor-sobiecki_avatar-96x96.jpg","contentUrl":"https:\/\/www.digitalforensics.com\/blog\/wp-content\/uploads\/2025\/07\/viktor-sobiecki_avatar-96x96.jpg","caption":"Viktor Sobiecki"},"description":"Dr. Viktor Sobiecki currently serves as the Chief Technology Officer (CTO) at Digital Forensics Corporation, where responsibilities span the leadership of advanced cybersecurity initiatives, data breach incident responses, and corporate strategic planning. Professional career has been driven by the intersection of innovation and practical application, particularly in the domains of cybersecurity and cyber crime investigations. He holds a Ph.D. in Computer Science and has contributed extensively to academic and industry advancements through publications, patents, and technological solutions addressing complex real-world challenges. As a professional with over 25 years of experience in the fields of cybersecurity, artificial intelligence, and digital forensics his career spans roles in academic research, software development, corporate leadership, and expert consulting, giving me a comprehensive understanding of the technical, strategic, and practical dimensions of projects. Expertise spans a wide range of technical domains, including: \u2022 Data Breach Incident Response: Managing immediate responses to cybersecurity crises, including the containment and mitigation of threats \u2022 Corporate Strategy Development: Designing long-term strategies to enhance organizational resilience against emerging cyber threats. \u2022 Expert Testimony: Providing legal and technical expertise in high-profile cybersecurity cases. \u2022 Artificial Intelligence and Machine Learning: Designing and implementing algorithms for data analysis, pattern recognition, and anomaly detection. \u2022 Network Security and Data Integrity: Developing solutions to protect critical systems from cyber threats, including encryption protocols and intrusion detection systems. \u2022 Cloud Computing and Hybrid Infrastructures: Creating scalable, resilient architectures for data storage, processing, and security.","sameAs":["https:\/\/www.digitalforensics.com\/","https:\/\/www.linkedin.com\/in\/viktor-sobiecki\/"],"honorificPrefix":"Dr","jobTitle":"Chief Technology Officer (CTO)","worksFor":"Digital Forensics Corporation","url":"https:\/\/www.digitalforensics.com\/blog\/author\/visor\/"}]}},"_links":{"self":[{"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/posts\/1465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/users\/126"}],"replies":[{"embeddable":true,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/comments?post=1465"}],"version-history":[{"count":5,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/posts\/1465\/revisions"}],"predecessor-version":[{"id":15990,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/posts\/1465\/revisions\/15990"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/media\/1474"}],"wp:attachment":[{"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/media?parent=1465"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/categories?post=1465"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.digitalforensics.com\/blog\/wp-json\/wp\/v2\/tags?post=1465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}