USRE40477E1 - Reliable detection of LSB steganography in color and grayscale images - Google Patents
Reliable detection of LSB steganography in color and grayscale images Download PDFInfo
- Publication number
- USRE40477E1 USRE40477E1 US11/639,355 US63935506A USRE40477E US RE40477 E1 USRE40477 E1 US RE40477E1 US 63935506 A US63935506 A US 63935506A US RE40477 E USRE40477 E US RE40477E
- Authority
- US
- United States
- Prior art keywords
- message
- groups
- types
- alleged
- samples
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime, expires
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
- G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2201/00—General purpose image data processing
- G06T2201/005—Image watermarking
- G06T2201/0061—Embedding of the watermark in each block of the image, e.g. segmented watermarking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2201/00—General purpose image data processing
- G06T2201/005—Image watermarking
- G06T2201/0065—Extraction of an embedded watermark; Reliable detection
Definitions
- This invention relates to steganography.
- Steganography is the art of secret communication, whose purpose is to hide the very presence of a communication.
- this invention relates to the detection of hidden messages.
- Steganography differs from cryptography, whose goal is to make communication unintelligible to those who do not posses the right keys.
- digital images, videos, sound files, and other computer files that contain perceptually irrelevant or redundant information can be used as covers, that is, as carriers that hide secret messages embedded within. If one embeds a secret message into a cover-image, one obtains a “stego-image.”
- the stego-image cannot contain any detectable artifacts that result from embedding the secret message. If it does, a third party can use such artifacts to determine that a secret message lies within the stego-image. Once the third party can reliably detect the presence of the secret message, the steganographic tool becomes useless.
- LSB Least Significant Bit embedding
- the present inventors have developed a steganographic method to detect LSB embedding in 24-bit color images.
- This RQP method is based on analyzing close pairs of colors created by LSB embedding. It works reasonably well as long as the number of unique colors in the cover image is less than 30% of the number of pixels. The size of the secret message can be estimated only very roughly. The results become progressively unreliable once the number of unique colors exceeds roughly 50% of the number of pixels, as happens frequently for high resolution raw scans and images taken with digital cameras stored in an uncompressed format.
- Another disadvantage of the RQP method is that it cannot be modified for grayscale images.
- PoVs Pairs of Values
- An object of the present invention is to provide an efficient, accurate, and simple method to reliably detect LSB embedding.
- a further object of the present invention is to provide such a method to reliably detect LSB embedding in randomly scattered pixels.
- Still a further object of the present invention is to provide such a method to reliably detect LSB embedding where the randomly scattered pixels are in both 24-bit color images and 8-bit grayscale or color images.
- the present invention provides a system and a method that efficiently, accurately, and simply detect reliably least-significant-bit (“LSB”) embedding of a secret message in randomly scattered pixels.
- the system and method apply to both 24-bit color images and 8-bit grayscale or color images.
- Many commercial steganographic programs use Least Significant Bit embedding (LSB) as the method of choice to hide messages in 24-bit, 8-bit color images and in grayscale images. They do so based on the common belief that changes to the LSBs of colors cannot be detected because of noise that is always present in digital images.
- the present invention reliably detects messages as short as 1% of the total number of pixels (assuming 1 bit per sample).
- the system and method of the present invention are fast, and they provide accurate estimates for the length of the embedded secret message.
- a method for detecting least significant bit (“LSB”) embedding of a message hidden in randomly scattered samples of an alleged cover image comprises the steps of:
- apparatus for detecting least significant bit (“LSB”) embedding of a message hidden in randomly scattered samples of an alleged cover image comprises means for dividing the alleged cover image into a plurality of disjoint groups of adjacent samples; first means for defining effective for defining a discrimination function that assigns a real number to each member of the plurality, thereby capturing the smoothness of each of the groups; second means for defining effective for defining on the plurality at least one invertible operation that comprises a permutation of sample values, whereby values of the samples are invertibly perturbed by a small amount; means for applying the discrimination function and the flipping operation to define in the plurality three types of sample groups, (R)egular, (S)ingular, and (U)nusable, each of the types being defined for both positive and negative operations; means for plotting both positive and negative R and S for the alleged cover image on an RS diagram; means for constructing four curves of the RS diagram; means for calculating the intersection
- a computer-readable storage medium embodies program instructions for a method for detecting least significant bit (“LSB”) embedding of a message hidden in randomly scattered samples of an alleged cover image, the method comprising the steps of: dividing the alleged cover image into a plurality of disjoint groups of adjacent samples; defining a discrimination function that assigns a real number to each member of the plurality, thereby capturing the smoothness of each of the groups; defining on the plurality at least one invertible operation that comprises a permutation of sample values, whereby values of the samples are invertibly perturbed by a small amount; applying the discrimination function and the flipping operation to define in the plurality three types of sample groups, (R)egular, (S)ingular, and (U)nusable, each of the types being defined for both positive and negative operations; plotting both positive and negative R and S for the alleged cover image on an RS diagram; constructing four curves of the RS diagram and calculating their intersections by
- FIG. 1 is a diagram of an image taken by a digital camera in which the regular groups of pixels (R) and the singular groups (S) are plotted as functions of the number of flipped pixels.
- FIG. 2 is a histogram of the initial bias (in percent of the total number of pixels) in 331 original cover images, of size 250 ⁇ 350 pixels, stored in JPEG format.
- FIG. 3 is a flow diagram showing the steps of an embodiment of the invention.
- the present invention can reliably detect messages embedded in this class of images and accurately estimate the message length.
- the novel steganalytic technique of the present invention which detects LSB embedding in color and grayscale images, originates in analyzing the capacity for lossless data embedding in the LSBs. For most images, the LSB plane is essentially random; it does not contain any easily recognizable structure. Thus classical statistical quantities constrained to the LSB plane cannot reliably capture the degree of randomization. Randomizing the LSBs decreases the lossless capacity in the LSB plane. It has a completely different influence on the capacity for embedding that is not constrained to one bit-plane. Thus the lossless capacity is a sensitive measure of the degree of randomization of the LSB plane.
- the lossless capacity reflects the fact that the LSB plane, even though it looks random, is nevertheless related to the other bit-planes. This relationship, however, is not linear but nonlinear, and the lossless capacity measures this relationship. Thus it can be used to detect steganography.
- the discrimination function captures the smoothness or “regularity” of the group of pixels G.
- the noisier the group of pixels G (x 1 , . . . , x n ), the larger the value of the discrimination function becomes. For example, we choose the ‘variation’ of the group of pixels (x 1 , . . .
- F flipping
- F will be a permutation of gray levels that consists entirely of two-cycles.
- the permutation F 1 0 ⁇ 1, 2 ⁇ 3, . . . , 254 ⁇ 255 corresponds to flipping (negating) the LSB of each gray level.
- F ⁇ and the flipping operation F to define three types of pixel groups: R, S, and U:
- FIG. 1 shows an RS-diagram of an image taken by a digital camera.
- the x-axis is the message length m, that is, the percentage of pixels with flipped LSBs.
- randomizing the LSB plane forces the difference between R M and S M to zero as the length m of the embedded message increases.
- R M @S M R M ⁇ S M This is equivalent to saying that the lossless embedding capacity in the LSB plane is zero (See Fridrich, Goljan, and Du, “Distortion-free Data Embedding”).
- randomizing the LSB plane has the opposite effect on R ⁇ M and S ⁇ M . Their difference increases with the length m of the embedded message.
- the RS diagram of FIG. 1 shows R M , S M , R ⁇ M , and S ⁇ M as functions of the number of pixels with flipped LSBs.
- the table below shows the four types and the number of R, S, and U groups under F 1 and F ⁇ 1 for each type. From the table, one can see that, while randomizing LSBs tends to equalize the number of R and S groups in each clique under F 1 , it increases the number of R groups and decreases the number of S groups under F ⁇ 1 .
- the new steganalytic technique of the present invention which we call the RS technique, is to estimate the four curves of the RS diagram of FIG. 1 and calculate their intersection by extrapolation.
- the general shape of the four curves in the diagram varies with the cover-image from almost perfectly linear to curved.
- Our experiments show that the R ⁇ M and S ⁇ M curves are well-modeled with straight lines; the inner curves R M and S M can be reasonably well approximated with second degree polynomials.
- the parameters of the curves can be determined from the points marked in FIG. 1 . If we have a stego-image with a message of an unknown length p (in percent of pixels) embedded in the LSBs of randomly scattered pixels, our initial measurements of the number of R and S groups correspond to the points R M (p/2), S M (p/2), R ⁇ M (p/2), and S ⁇ M (p/2) (see FIG. 1 ). The factor of one half comes from the fact that, if the message is a random bit-stream, on average only one half of the pixels will be flipped.
- the points R M (p/2), R M (1 ⁇ 2), R M (1 ⁇ p/2), and S M (p/2), S M (1 ⁇ 2), S M (1 ⁇ p/2) determine two parabolas. Each parabola and a corresponding line intersect to the left. The arithmetic average of the x coordinates of both intersections allows us to estimate the unknown message length p.
- the first is initial bias. Random variations can cause a cover image that contains no hidden message to indicate a small message length. This initial non-zero bias could be both positive and negative, and it puts a limit on the theoretical accuracy of the steganalytic technique of the present invention.
- This initial bias for a database of 331 grayscale JPEG images, which yielded a Gaussian distribution with a standard deviation of 0.5%, as shown in FIG. 2 . Smaller images tend to have higher variation in the initial bias because they have a smaller number of R and S groups. Scans of half-toned images and noisy images exhibit larger variations in the bias as well.
- the bias is typically very low for JPEG images, uncompressed images obtained by a digital camera, and high resolution scans. As another rule of thumb, we have found that color images exhibit larger variation in the initial bias than grayscale images.
- the RS Steganalysis technique is more accurate for messages that are randomly scattered in the stego-image than for messages concentrated in a localized area of the image. To address this issue, one can apply the same algorithm to a sliding rectangular region of the image.
- step 40 the cover image is divided into a plurality of disjoint groups of adjacent samples.
- step 42 a discrimination function is defined that assigns a real number to each member of the disjoint groups, thereby capturing the smoothness of the groups.
- step 44 at least one invertible operation is defined on the disjoint groups, with the invertible operation including a permutation of a sample values, whereby values of the samples are invertibly perturbed by a small amount.
- step 46 the discrimination function and the invertible operation are applied to define in step 48 three types of sample groups in the disjoint groups, i.e., (R)egular, (S)ingular, and (U)nusable, with each of the types being defined for both positive and negative operations.
- step 50 both positive and negative R and S are plotted for the alleged cover image on an RS diagram, after which four curves of the RS diagram are constructed and their intersections are calculated by extrapolation in step 52 .
- step 54 the existence or nonexistence of a secret message is determined from the intersections.
- the RS Steganalysis technique of the present invention is applicable to most commercial steganographic software products. We have tested the RS steganalytic technique on a small sample of images, processed with different software products, and with different message sizes. In all cases, stego-images were readily distinguished from original cover images, and the estimated message length was within a few percent of the actual message length. We believe that our technique is equally applicable to GIFs with randomly scattered messages.
- the present invention reliably detects messages shorter than 0.05 bits per pixel embedded in most cover images. For high quality images from a scanner or digital camera (the types most likely to be used for covert communication), even shorter messages (0.01 bits per pixel) can be reliably detected. Based on our experiments, we recommend a steganographic capacity of 0.005 bits per pixel as safe for LSB steganography, because our technique cannot reliably detect messages shorter than 0.005 bits per pixel. This upper bound is more than 100 times smaller than the bound found by Chandramouli and Memon. Thus, we can say that the present invention offers a 100-fold improvement over the prior art. The prior art therefore teaches away from the present invention.
Abstract
Description
We can design other discrimination functions based on models of or statistical assumptions about the cover image.
F−1(x)=F1(x+1)−1 for all x. (1a)
For completeness, we also define F0 as the identity permutation F(x)=x for all xÎP. We use the discrimination function ƒ and the flipping operation F to define three types of pixel groups: R, S, and U:
-
- Regular groups: GÎRÛG∈Rƒ(F(G))>ƒ(G)
- Singular groups: GÎSÛG∈Sƒ(F(G))<ƒ(G)
- Unusable groups: GÎUÛG∈Uƒ(F(G))=ƒ(G).
In the expressions above, F(G) means that the flipping function F is applied to the components of the vector G=(x1, . . . , xn). We may wish to apply different flipping to different pixels in the group G. The assignment of flipping to pixels can be captured with a mask M, which is a n-tuple with values −1, 0, and 1. The flipped group F(G) is defined as (FM(1)(x1), FM(2)(x2), . . . , FM(n)(xn)). The purpose of the flipping function F is to perturb the pixel values in an invertible way by some small amount, thereby simulating the act of invertibly adding noise. In typical pictures, adding a small amount of noise (i.e., flipping by a small amount) will lead to an increase rather than a decrease in the discrimination function. Thus, the total number of regular groups will be larger than the total number of singular groups. This bias allows for lossless imperceptible embedding of a potentially large amount of information (more details may be found in J. Fridrich, M. Goljan, and R. Du, “Distortion-free Data Embedding”, 4th Information Hiding Workshop, Pittsburgh, Pa., Apr. 25-27, 2001).
RM@R−M and SM@S−M RM ≅R −M and S M ≅S −M (2)
TABLE 1 | ||
Clique type | F1 flipping | F−1 flipping |
r = s = t | 2R, 2S, 4U | 8R |
r = s > t | 2R, 2S, 4U | 4R, 4U |
r < s > t | 4R, 4S | 4R, 4S |
r > s < t | 8U | 8U |
2(d1+d0)x2+(d−0−d−1−d1−3d0)x+d0−d−0=0,
where d0=RM(p/2)−SM(p/2), d1=RM(1−p/2)−SM(1−p/2), d−0=R−M(p/2)−S−M(p/2), and d−1=R−M(1−p/2)−S−M(1−p/2).
p=x/(x−½).
TABLE 2 |
Initial bias and estimated number of pixels with flipped LSBs |
for the first test image |
Red (%) | Green (%) | Blue (%) | ||
Cover image | 2.5 (0.0) | 2.4 (0.0) | 2.6 (0.0) | ||
|
10.6 (9.8) | 13.3 (9.9) | 12.4 (9.8) | ||
|
13.4 (10.2) | 11.4 (10.2) | 10.3 (10.2) | ||
|
12.9 (10.0) | 13.8 (10.1) | 13.0 (10.0) | ||
TABLE 3 |
Initial bias and estimated number of pixels with flipped LSBs |
for the second test image. |
Red (%) | Green (%) | Blue (%) | ||
Cover image | 0.00 (0.00 | 0.17 (0.00) | 0.33 (0.00) | ||
|
2.41 (2.44) | 2.70 (2.46) | 2.78 (2.49) | ||
|
2.45 (2.45) | 2.62 (2.43) | 2.75 (2.44) | ||
|
2.44 (2.46) | 2.62 (2.46) | 2.85 (2.45) | ||
Claims (40)
2(d1+d0)x2+(d−0−d−1−d1−3d0)x+d0−d−0=0,
p=x/(x−½).
2(d1+d0)x2+(d−0−d−1−d1−3d0)x+d0−d−0=0,
p=x/(x−½).
2(d1+d0)x2+(d−0−d−1−d1−3d0)x+d0−d−0=0,
p=x/(x−½).
2 (d 1 +d 0)x 2+(d −0 −d −1 −d 1 −3d 0)x−d −0 =0,
p=x/(x− 1/2 ).
2 (d 1 +d 0)x 2+(d −0 −d −1 −d 1 −3d 0)x−d −0 =0,
p=x/(x− 1/2 ).
2 (d 1 +d 0)x 2+(d −0 −d −1 −d 1 −3d 0)x−d −0 =0,
p=x/(x− 1/2 ).
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/639,355 USRE40477E1 (en) | 2001-06-22 | 2006-12-14 | Reliable detection of LSB steganography in color and grayscale images |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/887,805 US6831991B2 (en) | 2001-06-22 | 2001-06-22 | Reliable detection of LSB steganography in color and grayscale images |
US11/639,355 USRE40477E1 (en) | 2001-06-22 | 2006-12-14 | Reliable detection of LSB steganography in color and grayscale images |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/887,805 Reissue US6831991B2 (en) | 2001-06-22 | 2001-06-22 | Reliable detection of LSB steganography in color and grayscale images |
Publications (1)
Publication Number | Publication Date |
---|---|
USRE40477E1 true USRE40477E1 (en) | 2008-09-02 |
Family
ID=25391898
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/887,805 Ceased US6831991B2 (en) | 2001-06-22 | 2001-06-22 | Reliable detection of LSB steganography in color and grayscale images |
US11/639,355 Expired - Lifetime USRE40477E1 (en) | 2001-06-22 | 2006-12-14 | Reliable detection of LSB steganography in color and grayscale images |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/887,805 Ceased US6831991B2 (en) | 2001-06-22 | 2001-06-22 | Reliable detection of LSB steganography in color and grayscale images |
Country Status (1)
Country | Link |
---|---|
US (2) | US6831991B2 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105719225A (en) * | 2015-12-31 | 2016-06-29 | 杨春芳 | Image LSB matching steganography secret key recovery method based on wavelet absolute moment |
Families Citing this family (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP4178647B2 (en) * | 1999-02-15 | 2008-11-12 | 松下電器産業株式会社 | Digital information embedding / extracting apparatus and method, and recording medium storing a program for executing the method |
DE102004053129A1 (en) * | 2004-10-29 | 2006-05-04 | Technische Universität Dresden | Method for generating data provided for steganalysis, involves scanning carrier medium along spatial curve |
US7664967B2 (en) * | 2004-12-22 | 2010-02-16 | Borland Software Corporation | Development system with methodology providing information hiding in executable programs |
US7885470B2 (en) * | 2007-01-19 | 2011-02-08 | New Jersey Institute Of Technology | Method and apparatus for steganalysis for texture images |
WO2008105569A1 (en) * | 2007-02-28 | 2008-09-04 | Korea University Industrial & Academic Collaboration Foundation | Method for encoding and decoding for steganography |
TW201135663A (en) * | 2010-04-13 | 2011-10-16 | Univ Nat Chiao Tung | A covert communication method via PNG images based on the information sharing technique |
US9549197B2 (en) * | 2010-08-16 | 2017-01-17 | Dolby Laboratories Licensing Corporation | Visual dynamic range timestamp to enhance data coherency and potential of metadata using delay information |
CN102298766A (en) * | 2011-09-21 | 2011-12-28 | 北京工业大学 | Detection method for LSB information hiding based on weighted steganographic image |
CN103236265B (en) * | 2013-04-08 | 2015-10-14 | 宁波大学 | A kind of Stego-detection method for MP3Stegz |
CN103279914A (en) * | 2013-05-27 | 2013-09-04 | 深圳大学 | Image compression sensing steganography method and device based on frog-leaping optimization |
CN106530204B (en) * | 2016-11-21 | 2020-02-18 | 西华大学 | Adaptive image information hiding method based on critical value |
CN107947898B (en) * | 2017-11-15 | 2020-08-07 | 深圳大学 | Information detection method and device based on optimized grouping variance and receiving equipment |
US10685171B2 (en) * | 2018-01-31 | 2020-06-16 | Mcafee, Llc | Steganographic encoding detection and remediation |
US10699358B2 (en) * | 2018-02-22 | 2020-06-30 | Mcafee, Llc | Image hidden information detector |
CN109920014B (en) * | 2019-02-27 | 2022-10-28 | 中国科学技术大学 | 3D grid model steganography method |
WO2023061563A1 (en) * | 2021-10-12 | 2023-04-20 | Xonetix Ag | Data files embedding steganographic repeats of palindromic fingerprints for authorship protection |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5613004A (en) * | 1995-06-07 | 1997-03-18 | The Dice Company | Steganographic method and device |
US6038526A (en) * | 1998-06-24 | 2000-03-14 | The United States Of America As Represented By The Secretary Of The Navy | Method for detecting weak signals in a non-gaussian and non-stationary background |
US6064764A (en) * | 1998-03-30 | 2000-05-16 | Seiko Epson Corporation | Fragile watermarks for detecting tampering in images |
US6185312B1 (en) * | 1997-01-28 | 2001-02-06 | Nippon Telegraph And Telephone Corporation | Method for embedding and reading watermark-information in digital form, and apparatus thereof |
-
2001
- 2001-06-22 US US09/887,805 patent/US6831991B2/en not_active Ceased
-
2006
- 2006-12-14 US US11/639,355 patent/USRE40477E1/en not_active Expired - Lifetime
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5613004A (en) * | 1995-06-07 | 1997-03-18 | The Dice Company | Steganographic method and device |
US6185312B1 (en) * | 1997-01-28 | 2001-02-06 | Nippon Telegraph And Telephone Corporation | Method for embedding and reading watermark-information in digital form, and apparatus thereof |
US6064764A (en) * | 1998-03-30 | 2000-05-16 | Seiko Epson Corporation | Fragile watermarks for detecting tampering in images |
US6038526A (en) * | 1998-06-24 | 2000-03-14 | The United States Of America As Represented By The Secretary Of The Navy | Method for detecting weak signals in a non-gaussian and non-stationary background |
Non-Patent Citations (9)
Title |
---|
Analysis of LSB Based Image Steganography Techniques, Presened Oct. 7-10, 2001. * |
Distortion-Free Data Embedding for Images, Miroslav Goljan, Presented Apr. 25-27<SUP>th</SUP>, 2001. * |
Exploring Steganography: Seeing the Unseen, Neil F. Johnson, 1998. * |
High Capacity Despite Better Steganalysis, Andreas Westfeld, Presented Sep. 28<SUP>th </SUP>-Oct. 1, 1999. * |
IEEE-MultiMedia, Detecting LSB Steganography in Color and Gray-Scale Images, 2001 IEEE. * |
Practical invisibility in digital Communication, Tuomas Aura, Nov. 1995. * |
Steganalysis Based on JPED Compatability-Jessica Fridrich, Presented Aug. 20-24, 2001. * |
Steganalysis of Images Created Using Current Steganography Software, Neil F. Johnson. * |
Steganalysis of LSB Encoding in Color Images, Jessica Fridrich, Presented Jul. 31-Aug. 2, 2000. * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105719225A (en) * | 2015-12-31 | 2016-06-29 | 杨春芳 | Image LSB matching steganography secret key recovery method based on wavelet absolute moment |
CN105719225B (en) * | 2015-12-31 | 2018-12-11 | 杨春芳 | A kind of key recovery method of the LSB Matching steganography based on small echo absolute moment |
Also Published As
Publication number | Publication date |
---|---|
US6831991B2 (en) | 2004-12-14 |
US20030026447A1 (en) | 2003-02-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
USRE40477E1 (en) | Reliable detection of LSB steganography in color and grayscale images | |
Fridrich et al. | Reliable detection of LSB steganography in color and grayscale images | |
Huang et al. | Improved DCT-based detection of copy-move forgery in images | |
Fridrich et al. | Practical steganalysis of digital images: state of the art | |
Fridrich et al. | Detecting LSB steganography in color, and gray-scale images | |
Piva | An overview on image forensics | |
Fridrich et al. | Steganalysis based on JPEG compatibility | |
Fridrich et al. | Quantitative steganalysis of digital images: estimating the secret message length | |
US20230215197A1 (en) | Systems and Methods for Detection and Localization of Image and Document Forgery | |
Atta et al. | A high payload steganography mechanism based on wavelet packet transformation and neutrosophic set | |
Lin | Watermarking and digital signature techniques for multimedia authentication and copyright protection | |
CN101059863A (en) | Embed and detection method for identifying water mark, its system and uses | |
Ketenci et al. | Copy-move forgery detection in images via 2D-Fourier transform | |
Taspinar et al. | Camera fingerprint extraction via spatial domain averaged frames | |
Conotter et al. | Forensic detection of processing operator chains: Recovering the history of filtered JPEG images | |
Kwok et al. | Alternative anti-forensics method for contrast enhancement | |
Chrysochos et al. | Reversible image watermarking based on histogram modification | |
Hadmi et al. | A robust and secure perceptual hashing system based on a quantization step analysis | |
JP4602983B2 (en) | Method and apparatus for embedding and detecting structured watermarks | |
Hamdy et al. | Quantization table estimation in JPEG images | |
Marçal et al. | A steganographic method for digital images robust to RS steganalysis | |
Ustubıoglu et al. | Image forgery detection using colour moments | |
Qu et al. | A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensics | |
Sun et al. | The detecting system of image forgeries with noise features and EXIF information | |
Xiao et al. | A semi-fragile watermarking tolerant of Laplacian sharpening |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
FEPP | Fee payment procedure |
Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
FPAY | Fee payment |
Year of fee payment: 8 |
|
AS | Assignment |
Owner name: THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, NEW YORK Free format text: CHANGE OF NAME;ASSIGNOR:THE RESEARCH FOUNDATION OF STATE UNIVERSITY OF NEW YORK;REEL/FRAME:031896/0589 Effective date: 20120619 Owner name: THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY O Free format text: CHANGE OF NAME;ASSIGNOR:THE RESEARCH FOUNDATION OF STATE UNIVERSITY OF NEW YORK;REEL/FRAME:031896/0589 Effective date: 20120619 |
|
FPAY | Fee payment |
Year of fee payment: 12 |