The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.
","abstract_html":"<p>The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.</p>","abstract_has_math":false,"creators":["Alzamil, Lubna"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":null,"degree_discipline":"Computational Science","degree_department":null,"school":null,"contributors":["Razvan Andonie","Szilard Vajda","Boris Kovalerchuk"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:37:48Z","subjects":["photo manipulation","social media","social networks","influence campaigns","deepfake","detection","Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.cwu.edu/etd/1361","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Razvan Andonie","Szilard Vajda","Boris Kovalerchuk"]},{"key":"dc:creator","label":"Author","values":["Alzamil, Lubna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-17T07:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["photo manipulation","social media","social networks","influence campaigns","deepfake","detection","Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.cwu.edu/etd/1361"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.
"]},{"key":"dc:title","label":"Title","values":["Image Forgery Detection with Machine Learning"]}]}],"canonical_facts":{"dc:contributor":["Razvan Andonie","Szilard Vajda","Boris Kovalerchuk"],"dc:creator":["Alzamil, Lubna"],"dc:date.available":["2020-06-17T07:00:00Z"],"dc:description.abstract":["The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.
"],"dc:identifier":["https://digitalcommons.cwu.edu/etd/1361"],"dc:subject":["photo manipulation","social media","social networks","influence campaigns","deepfake","detection","Computer Engineering"],"dc:title":["Image Forgery Detection with Machine Learning"],"dc:type":["Text"],"thesis:degree_discipline":["Computational Science"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:37:48Z"}