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		<title>Hybrid AI Reaches Near-Perfect Accuracy in the Hunt for Doctored Images</title>
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		<pubDate>Sun, 04 Oct 2026 11:59:35 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[benchmark datasets]]></category>
		<category><![CDATA[CASIA dataset]]></category>
		<category><![CDATA[CNN]]></category>
		<category><![CDATA[copy-move forgery]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[deepfake detection]]></category>
		<category><![CDATA[digital forensics]]></category>
		<category><![CDATA[GAN]]></category>
		<category><![CDATA[image forgery detection]]></category>
		<category><![CDATA[metaheuristic optimization]]></category>
		<category><![CDATA[particle swarm optimization]]></category>
		<category><![CDATA[transformers]]></category>
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					<description><![CDATA[A new systematic review finds that hybrid deep learning and metaheuristic optimization frameworks achieve 98 to 100 percent accuracy in detecting copy-move image forgeries, while identifying robustness and generalization as the field's key remaining challenges.]]></description>
		
		
		
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