sameersyed/Defence_FrameWork
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target_system�real_world_impact�attack_success_rate�detection_difficultyzFace Recognition Spoofingz3Printed adversarial glasses fool facial recognitionzAdversarial Accessoryz Biometric Authentication Systemz(Unauthorized access to secure facilitiesz73%�MEDIUMzObject Detection Evasionz8Adversarial patterns make objects invisible to detectorszAdversarial Camouflagez Security Surveillance SystemzIntruders evade detectionz68%�� traffic_sign�face_recognition�object_detection�� case_studies)�selfs �2D:\Security_app\backend\physical_world_analyzer.py�__init__� PhysicalWorldAnalyzer.__init__ s` � � .� [� ;�!C�%L�',�(.� � 4� T� 6�!B�%O�',�(0�!� 3� Y� 7�!?�%@�',�(.�!�' 7��� c � � XR ; a SS0$ U R U nUU R U5 U R U5 U R U5 S.$ )z/Analyze specific physical-world attack scenario�errorzUnknown scenario)�scenario�vulnerability_analysis�defense_strategies�mitigation_steps)r �_analyze_vulnerabilities�
_get_defenses�_get_mitigation)r � scenario_id�cases r �analyze_scenario�&PhysicalWorldAnalyzer.analyze_scenario# sc � � � /� /� /�� /�0� 0�� � ��-�� �&*�&C�&C�K�&P�"&�"4�"4�[�"A� $� 4� 4�[� A� 8� 9r c �> � / SQ/ SQ/ SQS.nUR U/ 5 $ )z$Analyze why physical attacks succeed)z*Model trained only on clean digital imagesz'No robustness to physical perturbationsz%High confidence on adversarial inputsz Lack of context-aware validation)z0Insufficient training on adversarial accessoriesz Over-reliance on facial featureszNo liveness detectionz Weak multi-factor authentication)z*Texture-based classification vulnerabilityz No temporal consistency checkingz Single-frame decision makingzLack of anomaly detectionr ��get)r r# �vulnerabilitiess r r �.PhysicalWorldAnalyzer._analyze_vulnerabilities1 s- � � � !� !�10��( �"�"�;��3�3r c � � SSSS.SSSS.SSS S./S11SS S.S SS
S.SSSS./SSSS.SSSS.SSSS./S.nUR U/ 5 $ )z$Get applicable defenses for scenarioz0Adversarial Training with Physical Perturbationsr z8Train on images with physical noise, lighting variations)r �
effectiveness�implementationzMulti-View Verificationr z(Use multiple camera angles for consensuszContext-Aware Validationz(Verify predictions against GPS, map datazLiveness Detectionz+Detect 3D depth, eye movement, skin texturezMulti-Modal Authenticationz,Combine face + voice + behavioral biometricsz Adversarial Accessory Detectionz1Train classifier to detect suspicious accessoriesz Temporal Consistency Checkingz-Track objects across frames, detect anomalieszEnsemble Detectionz0Use multiple detection models, require consensuszTexture-Invariant Featuresz*Focus on shape, motion rather than texturer r( )r r# �defensess r r! �#PhysicalWorldAnalyzer._get_defensesI s� � �12 O�%+�&`�� 6�%-�&P�� 7�%+�&P�� �& 1�%+�&S�� 9�%+�&T�� >�%-�&Y��!�& <�%+�&U�� 1�%-�&X�� 9�%-�&R��!�G413��j �|�|�K��,�,r c �> � / SQ/ SQ/ SQS.nUR U/ 5 $ )z Get step-by-step mitigation plan)z/Immediate: Add redundant sensors (LIDAR, radar)z7Short-term: Retrain with augmented physical attack dataz2Long-term: Implement multi-modal perception fusion)z-Immediate: Enable multi-factor authenticationz,Short-term: Deploy liveness detection modulez+Long-term: Upgrade to 3D facial recognition)z.Immediate: Enable temporal tracking and alertsz,Short-term: Deploy ensemble detection systemz-Long-term: Implement physics-based validationr r( )r r# �14mitigations r r" �%PhysicalWorldAnalyzer._get_mitigation� s+ � � �15!�16!�17�18�" �~�~�k�2�.�.r c � � SU; a SUS 3$ US n/ nUR S5 UR S5 UR SUS S35 UR S US19 S35 UR S US S
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35 M UR S#5 UR S$5 UR S%5 UR S&5 S'R U5 $ )(z.Generate physical-world attack analysis reportr u ❌ Error: r u' 🌍 Physical-World Attack Case Study20 21u� ────────────────────────────────────────────────────────────22 23u 📋 Scenario: r z24 25z
Description: r u 🎯 Target System: r26 �27u ⚔️ Attack Type: r u 📊 Success Rate: r u 🔍 Detection Difficulty: r
u 💥 Real-World Impact:28u • r u 🔓 Why This Attack Succeeds:29 30r r z. u 🛡️ Defense Strategies:31 32r u • Effectiveness: r- u • Implementation: r. u 🗺️ Mitigation Roadmap:33 34r u 35💡 Key Takeaway:36z@Physical-world attacks exploit the gap between digital training37zCand real-world deployment. Defense requires multi-layered approach38zFcombining robust training, runtime validation, and redundant systems.39� )�append� enumerate�join)r �analysisr$ �report�i�vuln�defense�steps r �generate_report�%PhysicalWorldAnalyzer.generate_report� sy � � �h� � ��'�!2� 3�4� 4��40�#�����
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