Visible to the public A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification

TitleA PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification
Publication TypeJournal Article
Year of Publication2020
AuthorsZheng, Y., Cao, Y., Chang, C.
JournalIEEE Transactions on Information Forensics and Security
Keywordsacquisition device identification, benign content, Bernoulli random space, Camera Identification, Cameras, CASIA database, CMOS image sensor-based PUF, CMOS image sensors, copy protection, digital content creation, digital devices, digital forensics, digital image forensics, digital images, feature extraction, Forensics, Forgery, forgery content detection, geometric transformations, Human Behavior, image acquisition timestamp, image capture, Image forensics, image hash, image processing operations, image tampering problems, image watermarking, information forensics, integrated circuit modelling, invariant image features, Metrics, object detection, perceptual data-device hash, Perceptual Image Hash, physical unclonable function, pubcrawl, PUF-based data-device hash, resilience, Resiliency, Scalability, size 180.0 nm, source camera identification, standard content-preserving manipulations, tamper-resistant random PUF response, tampered image detection, TSMC technology, video coding, video footage, video watermarking, Watermarking
AbstractWith the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 360deg) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF.
Citation Keyzheng_puf-based_2020