Qu et al., 2014 - Google Patents
A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensicsQu et al., 2014
View PDF- Document ID
- 3788618824462242486
- Author
- Qu Z
- Luo W
- Huang J
- Publication year
- Publication venue
- Science China Information Sciences
External Links
Snippet
Non-intrusive digital image forensics (NIDIF) is a novel approach to authenticate the trustworthiness of digital images. It works by exploring varieties of intrinsic characteristics involved in the digital imaging, editing, storing processes as discriminative features to reveal …
- 238000007906 compression 0 title abstract description 38
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4642—Extraction of features or characteristics of the image by performing operations within image blocks or by using histograms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2201/00—General purpose image data processing
- G06T2201/005—Image watermarking
- G06T2201/0202—Image watermarking whereby the quality of watermarked images is measured; Measuring quality or performance of watermarking methods; Balancing between quality and robustness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Park et al. | Double JPEG detection in mixed JPEG quality factors using deep convolutional neural network | |
Chen et al. | Blind detection of median filtering in digital images: A difference domain based approach | |
US20230215197A1 (en) | Systems and Methods for Detection and Localization of Image and Document Forgery | |
Qureshi et al. | A bibliography of pixel-based blind image forgery detection techniques | |
Yang et al. | Source camera identification based on content-adaptive fusion residual networks | |
Wang et al. | Image tampering detection based on stationary distribution of Markov chain | |
Shen et al. | Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG images | |
Singh et al. | Fast and efficient region duplication detection in digital images using sub-blocking method | |
Qureshi et al. | Bibliography of digital image anti‐forensics and anti‐anti‐forensics techniques | |
Conotter et al. | Forensic detection of processing operator chains: Recovering the history of filtered JPEG images | |
Hou et al. | Detection of hue modification using photo response nonuniformity | |
Hakimi et al. | Image-splicing forgery detection based on improved lbp and k-nearest neighbors algorithm | |
Gul et al. | SVD based image manipulation detection | |
Peng et al. | Median filtering forensics using multiple models in residual domain | |
Liu et al. | Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation | |
Taspinar et al. | Camera identification of multi-format devices | |
Dixit et al. | Copy-move forgery detection exploiting statistical image features | |
Swaminathan et al. | Component forensics | |
Sari et al. | The effect of error level analysis on the image forgery detection using deep learning | |
Qu et al. | A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensics | |
Kamenicky et al. | PIZZARO: Forensic analysis and restoration of image and video data | |
Li et al. | A robust approach to detect digital forgeries by exploring correlation patterns | |
Subrahmanyeswara Rao | A fuzzy fusion approach for modified contrast enhancement based image forensics against attacks | |
Ustubıoglu et al. | Image forgery detection using colour moments | |
Qiao et al. | Classifying between computer generated and natural images: An empirical study from RAW to JPEG format |