2 research outputs found
The image series forgery detection algorithm based on the camera pattern noise analysis
Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ Π½Π° Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΡΡΠΌΠ° ΠΌΠ°ΡΡΠΈΡΡ ΠΊΠ°ΠΌΠ΅ΡΡ. Π₯Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΡΡΠΌΠ° ΠΊΠ°ΠΌΠ΅ΡΡ (Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΡΡΠΌΠ°, Π²ΡΠ·Π²Π°Π½Π½ΠΎΠ³ΠΎ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΡΡ ΡΠΎΡΠΎΠΎΡΠΊΠ»ΠΈΠΊΠ°, PRNU) ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΠΏΡΡΠ΅ΠΌ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΡΠΌΠΎΠ²ΠΎΠΉ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠ΅ΠΉ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· ΡΠ΅ΡΠΈΠΈ Π½Π΅ΠΈΡΠΊΠ°ΠΆΠ΅Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. ΠΠ»Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΠΌΠΎΠ²Π°Ρ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠ°Ρ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π΅ΡΡΡ ΡΠΎ ΡΡΡΡΠΊΡΡΡΠ½ΡΠΌ ΡΡΠΌΠΎΠΌ ΠΊΠ°ΠΌΠ΅ΡΡ. Π ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈΡΡΠ»Π΅Π΄ΡΡΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠΌΠ°. ΠΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π² Π·Π°Π΄Π°ΡΠ΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΈΠΏΠ° 11Π΄ΡΠ±Π»ΠΈΠΊΠ°Ρ11ΠΈ Π·Π°Π΄Π°ΡΠ΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ Π²ΡΡΠ°Π²ΠΎΠΊ Ρ Π΄ΡΡΠ³ΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π½Π΅ Π²ΠΊΠ»ΡΡΠ΅Π½Π½ΡΡ
Π² cΠ΅ΡΠΈΡ.
In the paper, the image series forgery detection algorithm based on analysis of camera pattern noise is proposed. Distribution characteristics of the camera pattern noise are obtained by extracting the noise component of images from the non-tampered image series. A noise residual of a forgery image is compared with the camera pattern noise. We compare various noise filtering algorithms to choose the one that achieves the best performance of the proposed method. The proposed algorithm is tested both on examples of copy- move forgeries and forgery fragments which were inserted from an image not included into the image series.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° β18-01-00748-Π° ΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ ΠΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²Π° Π½Π°ΡΠΊΠΈ ΠΈ Π²ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π Π€ Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΡΠ°Π±ΠΎΡ Π½ΠΎ ΠΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΌΡ Π·Π°Π΄Π°Π½ΠΈΡ Π€ΠΠΠ¦ "ΠΡΠΈΡΡΠ°Π»Π»ΠΎΠ³ΡΠ°ΡΠΈΡ ΠΈ ΡΠΎΡΠΎΠ½ΠΈΠΊΠ°" Π ΠΠ (ΡΠΎΠ³Π»Π°ΡΠ΅Π½ΠΈΠ΅ β007-ΠΠ/Π§3363/26)
Detecting forgery of image time series based on the anomalies detection
ΠΡΠ½ΠΎΠ²Π½Π°Ρ ΡΡΠ°ΡΡΡΠΠ°ΡΡΠΎΡΡΠ°Ρ ΡΠ°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΡΠ΅Π΄Π½Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΡ
ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ β ΡΠ°Π»ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΉ β ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π² ΡΠ΅ΡΠΈΠΈ (Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ) ΡΠ½ΠΈΠΌΠΊΠΎΠ² ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ΅Π½Ρ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· ΡΡΠ΅Ρ
ΡΡΠ°ΠΏΠΎΠ². ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π΅ΡΡΡ ΡΡΠ΄ ΠΎΡΠΈΠ±ΠΎΠΊ ΡΠ΅ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΈ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΡΠ½ΠΈΠΌΠΊΠ° ΠΏΠΎ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠΌ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠ°ΠΌ Β«ΡΠΎΡΠ΅Π΄Π½ΠΈΡ
Β» ΡΠ½ΠΈΠΌΠΊΠΎΠ². ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΠΏΠΎ Π²ΡΠ΅ΠΌΡ ΡΠ½ΠΈΠΌΠΊΡ ΠΎΡΠΈΠ±ΠΊΠΈ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΡΡΡΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π° Π²ΡΠΎΡΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΈΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ. ΠΠ° Π·Π°ΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Β«ΠΏΠΎΠ΄ΠΎΠ·ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
Β» ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΠΊΠ°Π΄ΡΠ° ΠΎΡΠ±ΠΈΡΠ°ΡΡΡΡ ΡΠ΅, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡ ΡΠΎΠ±ΠΎΠΉ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ β ΡΠΎ Π΅ΡΡΡ Π² ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΌΡΡΠ»Π΅ ΠΌΠ°Π»ΠΎΠ²Π΅ΡΠΎΡΡΠ½Ρ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ, Π² ΠΎΡΠ»ΠΈΡΠΈΠ΅ ΠΎΡ ΡΡΠ΄Π° ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΡΠ°ΠΊΠΈΡ
Π°ΡΠ°ΠΊ, ΠΊΠ°ΠΊ Π΄ΡΠ±Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, Π²ΡΠ΅Π·ΠΊΠΈ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² Ρ Β«ΡΠΎΡΠ΅Π΄Π½ΠΈΡ
Β» ΡΠ½ΠΈΠΌΠΊΠΎΠ², Π²ΡΠ΅Π·ΠΊΠΈ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² Ρ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, Π½Π΅ Π²ΠΊΠ»ΡΡΠ΅Π½Π½ΠΎΠ³ΠΎ Π² ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΡ ΡΠ΅ΡΠΈΡ ΠΈ Ρ.ΠΏ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠΎ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Π°ΡΠ°ΠΊ.
Increasing complexity of image forgery methods is an actual problem nowadays. This problem rises due to the expansion of elds that use digital images in their work. Image time series show the dynamics of the scene and allow it to be compared over time. This paper proposes a new algorithm for detecting forgeries of single digital image in an image time series described a scene. First part of paper provides description of proposed algorithm consisted of three stages. The aim of rst stage is getting a set of errors that were computed during reconstruction of analyzed image using other images of series. Errors distribution histograms is constructed and estimated on the second stage. On the third stage, anomaly types are determined and decision rule for each anomaly type is set up. Finally, fragments of the analyzed image that are anomalies are selected as ' suspicious'. Second part of paper contains investigation results of intra-image copy-move and inter-image copy-move detection using the proposed algorithm