Image manipulation has acquired a fatal significance as digital falsifications have been introduced, and this threat has endangered credibility and trust in visual media. The conventional watermarking and hashing algorithms lack efficiency as far as the detection of more complex manipulations and transformations is concerned and the more diverse and intelligent models of detection should be introduced. The proposed study will enhance accuracy and robustness of image tampering detector by incorporating two algorithms, that is Adaptive Multi-Layer Feature Disentanglement (AMFD) that detects feature and Adaptive Hyperplane Evolution Classification (AHEC) that will classify the features. The data employed in the assessment is CASIA 2.0 and the suggested AMFD algorithm estimations the independent and discriminative characteristics by disentangling the multi-layer representations in a hierarchical way and reducing redundancy to improve the quality and uniqueness of the discovered features. To perform the optimal separation of the classes, as well as to classify tampered and authentic images correctly, the proposed AHEC algorithm classifies pieces separately using adaptively evolving hyperplanes on the feature space. The experimental outcomes suggest that both algorithms are more effective than the ones that are already known with AMFD implying increased compactness of features, ability to separate them, and structural similarity, and AHEC implying increased improvements in accuracy, sensitivity, precision, and robustness.