8 research outputs found
Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces
Recent advances in deep learning have enabled forensics researchers to
develop a new class of image splicing detection and localization algorithms.
These algorithms identify spliced content by detecting localized
inconsistencies in forensic traces using Siamese neural networks, either
explicitly during analysis or implicitly during training. At the same time,
deep learning has enabled new forms of anti-forensic attacks, such as
adversarial examples and generative adversarial network (GAN) based attacks.
Thus far, however, no anti-forensic attack has been demonstrated against image
splicing detection and localization algorithms. In this paper, we propose a new
GAN-based anti-forensic attack that is able to fool state-of-the-art splicing
detection and localization algorithms such as EXIF-Net, Noiseprint, and
Forensic Similarity Graphs. This attack operates by adversarially training an
anti-forensic generator against a set of Siamese neural networks so that it is
able to create synthetic forensic traces. Under analysis, these synthetic
traces appear authentic and are self-consistent throughout an image. Through a
series of experiments, we demonstrate that our attack is capable of fooling
forensic splicing detection and localization algorithms without introducing
visually detectable artifacts into an attacked image. Additionally, we
demonstrate that our attack outperforms existing alternative attack approaches.
Open Set Synthetic Image Source Attribution
AI-generated images have become increasingly realistic and have garnered
significant public attention. While synthetic images are intriguing due to
their realism, they also pose an important misinformation threat. To address
this new threat, researchers have developed multiple algorithms to detect
synthetic images and identify their source generators. However, most existing
source attribution techniques are designed to operate in a closed-set scenario,
i.e. they can only be used to discriminate between known image generators. By
contrast, new image-generation techniques are rapidly emerging. To contend with
this, there is a great need for open-set source attribution techniques that can
identify when synthetic images have originated from new, unseen generators. To
address this problem, we propose a new metric learning-based approach. Our
technique works by learning transferrable embeddings capable of discriminating
between generators, even when they are not seen during training. An image is
first assigned to a candidate generator, then is accepted or rejected based on
its distance in the embedding space from known generators' learned reference
points. Importantly, we identify that initializing our source attribution
embedding network by pretraining it on image camera identification can improve
our embeddings' transferability. Through a series of experiments, we
demonstrate our approach's ability to attribute the source of synthetic images
in open-set scenarios
VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces
Fake videos represent an important misinformation threat. While existing
forensic networks have demonstrated strong performance on image forgeries,
recent results reported on the Adobe VideoSham dataset show that these networks
fail to identify fake content in videos. In this paper, we show that this is
due to video coding, which introduces local variation into forensic traces. In
response, we propose VideoFACT - a new network that is able to detect and
localize a wide variety of video forgeries and manipulations. To overcome
challenges that existing networks face when analyzing videos, our network
utilizes both forensic embeddings to capture traces left by manipulation,
context embeddings to control for variation in forensic traces introduced by
video coding, and a deep self-attention mechanism to estimate the quality and
relative importance of local forensic embeddings. We create several new video
forgery datasets and use these, along with publicly available data, to
experimentally evaluate our network's performance. These results show that our
proposed network is able to identify a diverse set of video forgeries,
including those not encountered during training. Furthermore, we show that our
network can be fine-tuned to achieve even stronger performance on challenging
AI-based manipulations
Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools
We present a first of its kind dataset of overhead imagery for development
and evaluation of forensic tools. Our dataset consists of real, fully synthetic
and partially manipulated overhead imagery generated from a custom diffusion
model trained on two sets of different zoom levels and on two sources of
pristine data. We developed our model to support controllable generation of
multiple manipulation categories including fully synthetic imagery conditioned
on real and generated base maps, and location. We also support partial
in-painted imagery with same conditioning options and with several types of
manipulated content. The data consist of raw images and ground truth
annotations describing the manipulation parameters. We also report benchmark
performance on several tasks supported by our dataset including detection of
fully and partially manipulated imagery, manipulation localization and
classification
KIF4A: A potential biomarker for prediction and prognostic of prostate cancer
Purpose: To investigate the clinical relevance and biological function of the kinesin super-family protein 4A (KIF4A) expression in prostate cancer (PCa).
Methods: We examined 1) the relationship between the expression of KIF4A and clinico-pathological characteristics of PCa patients using a tissue microarray and the Cancer Genome Atlas database, 2) the prognostic value of KIF4A expression in patients using Kaplan-Meier plots and 3) the functions of KIF4A in LNCaP and DU145 cells, such as cell proliferation, cell cycle and cell apoptosis.
Results: Compared with normal prostate, the mRNA and protein expressions of KIF4A were up-regulated in PCa. The up-regulation expression rates of KIF4A in PCa were significantly related to the Gleason score (