TRAIGA

TRAIGA, or the Texas Responsible Artificial Intelligence Governance Act, is a state law regulating the development and deployment of artificial intelligence (AI) systems in Texas. Sponsored by Representative Giovanni Capriglione, the Act establishes a framework governing certain uses of AI, outlines prohibited uses, and creates obligations on state government entities, among other provisions. TRAIGA was signed into law in 2025 and took effect on January 1, 2026. The law applies to AI developers and deployers that conduct business in Texas or whose systems are used by Texas residents. It prohibits the intentional development or deployment of AI systems to incite harm, violate constitutional rights, engage in unlawful discrimination, and produce child sexual abuse material or unlawful deepfakes. TRAIGA also establishes the Texas Artificial Intelligence Council and creates a regulatory sandbox program. The Texas Attorney General is charged with enforcement. It has received attention as one of the first comprehensive AI-related laws enacted by a U.S. state. Legal analysts have compared it to the European Union (EU) Artificial Intelligence Act and the Colorado AI Act, noting its intent-based discrimination standard and narrower scope relative to those frameworks. == Background == In June 2023, Texas Governor Greg Abbott signed House Bill 2060, which created an Artificial Intelligence Advisory Council within the Texas Department of Information Resources. The Council was tasked with monitoring the use of AI systems across state government. Its membership included representatives from law enforcement, academia, and the legal profession. After submitting a report to state policymakers, the Council was disbanded in December 2024. Separately, the Texas House Select Committee on Artificial Intelligence and Emerging Technologies was created in 2023 to examine the political and social implications of artificial intelligence. Among its recommendations was the creation of a regulatory sandbox to allow for controlled testing of AI systems. This recommendation informed the regulatory sandbox provision included in TRAIGA. == History == In December 2024, Representative Capriglione introduced House Bill 1709, the Texas Responsible Artificial Intelligence Governance Act. The bill sought to create a statewide framework for artificial intelligence, including transparency requirements for companies deploying AI systems, restrictions on certain uses of AI, and the creation of a regulatory sandbox. Modeled in part on the EU Artificial Intelligence Act and the Colorado AI Act, House Bill 1709 focused on "high-risk" AI systems and included provisions addressing private sector liability. House Bill 1709 did not advance during the legislative session. Industry stakeholders raised concerns that several provisions were overly burdensome. The bill informed the development of a revised proposal, House Bill 149, also titled the Texas Responsible Artificial Intelligence Governance Act. The revised version removed requirements for private companies to notify consumers when they interact with AI systems and to conduct impact assessments, among other provisions. In April 2025, an amended version of House Bill 149 passed the Texas House of Representatives and was referred to the Senate Committee on Business and Commerce. The bill later received approval from both chambers, where the House voted on amendments adopted by the Senate. On May 31, 2025, the state legislature passed House Bill 149, one of several AI-related bills considered during the legislative session. Governor Abbott signed TRAIGA into law on June 22, 2025. During the legislative process, a proposed federal moratorium on state-level AI regulation initially raised questions about the enforceability of state AI laws, including TRAIGA. At the time of signing, Governor Abbott stated that Texas would ensure compliance with applicable federal requirements. In July 2025, the United States Senate voted to remove the proposed moratorium from federal legislation. The Act took effect on January 1, 2026. == Provisions == === Definitions and scope === TRAIGA applies to AI developers and deployers that advertise or conduct business in Texas, develop products used by Texas residents, or develop or deploy AI systems within the state. The Act also applies to Texas state and local government entities. The Act defines a developer as a person who develops an AI system and a deployer as one who deploys an AI system in Texas. Consumers are defined as Texas residents. The Act defines an artificial intelligence system as a machine-based system that "infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments." === Government use === The Act requires government agencies to provide consumers with plain language notices before interacting with AI systems. It also prohibits government agencies from using artificial intelligence systems to assign social scores to consumers. It also restricts the use of AI systems to identify individuals using biometric data without the individual’s consent. === Prohibitions === The Act prohibits the development or deployment of artificial intelligence systems intended to cause harm, self-harm, or criminal activity. It also prohibits the development or deployment of AI systems designed to violate constitutional rights or unlawfully discriminate based on protected classes. In addition, the Act prohibits the development or deployment of AI systems that are intended to produce or distribute child sexual abuse material or unlawful deepfakes. === Enforcement === Enforcement authority under the Act rests with the Texas Attorney General. The Act does not create a private right of action. The Act requires the Texas Attorney General to create an online complaint system where consumers may submit allegations of potential violations. The Attorney General can investigate complaints received through this system and may request information relevant to the operation of an AI system, including information about training data. Before initiating an enforcement action, the Attorney General must provide a written notice to the alleged violator, who is then provided with a 60-day period to cure the alleged violation. === Penalties === If a violation is not cured, the Act authorizes civil penalties. Penalties range from $10,000 to $12,000 per curable violation and from $80,000 to $200,000 per non-curable violation. The Act also authorizes additional penalties of $2,000 to $40,000 for each day the violation continues. If the Attorney General determines that a person certified or licensed by a state agency has violated the Act and recommends enforcement, the relevant agency may impose additional administrative sanctions, including license suspension or further monetary penalties. === Safe harbor === The Act provides an affirmative defense for AI developers and deployers who identify potential violations through internal testing or auditing or who demonstrate compliance with National Institute of Standards and Technology (NIST)'s Artificial Intelligence Risk Management Framework or a comparable risk management framework. The Act also affords protection to developers and deployers when a third party uses their AI systems in a way that violates the Act. === Texas Artificial Intelligence Council === The Act creates the Texas Artificial Intelligence Council to assist the state legislatures in evaluating artificial intelligence policy and oversight. The Council is charged with developing recommendations for state agencies regarding the use of AI systems and with overseeing the regulatory sandbox. TRAIGA gives the Council the ability to organize AI-related training for state entities and issue reports concerning artificial intelligence. The Council does not have binding rulemaking authority. The Council consists of seven members appointed by the governor, the lieutenant governor, and the speaker of the Texas House of Representatives. === Regulatory sandbox === The Act directs the Texas Department of Information Resources to create a regulatory sandbox program that allows participants to test AI systems under state supervision in a modified regulatory setting. To join the program, companies must submit applications that describe their AI systems and intended use. Approved participants may operate within the sandbox for up to 36 months. During that period, the Attorney General is restricted from initiating enforcement actions for certain categories of violations. == Reception == === Support === During legislative testimony, the Texas Public Policy Foundation stated that TRAIGA would benefit Texas businesses by reducing legal ambiguity and creating clearer compliance standards. Representatives of business groups also expressed support, stating that the Act would not impose overly burdensome regulations. The consum

Moving object detection

Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected. Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border, etc. == Definition == Moving object detection is to recognize the physical movement of an object in a given place or region. By acting segmentation among moving objects and stationary area or region, the moving objects' motion can be tracked and thus analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object detection is to find the foreground moving target(s), either in each video frame or only when the moving target shows the first appearance in the video. == Traditional methods == Among all the traditional moving object detection methods, we could categorize them into four major approaches: Background subtraction, Frame differencing, Temporal Differencing, and Optical Flow. === Frame differencing === Instead of using traditional approach, to use image subtraction operator by subtracting second and images afterwards, the frame differencing method makes comparisons between two successive frames to detect moving targets. === Temporal differencing === The temporal differencing method identifies the moving object by applying pixel-wise difference method with two or three consecutive frames.

Forking lemma

The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

Conjugate coding

Conjugate coding is a cryptographic tool, introduced by Stephen Wiesner in the late 1960s. It is part of the two applications Wiesner described for quantum coding, along with a method for creating fraud-proof banking notes. The application that the concept was based on was a method of transmitting multiple messages in such a way that reading one destroys the others. This is called quantum multiplexing and it uses photons polarized in conjugate bases as "qubits" to pass information. Conjugate coding also is a simple extension of a random number generator. At the behest of Charles Bennett, Wiesner published the manuscript explaining the basic idea of conjugate coding with a number of examples but it was not embraced because it was significantly ahead of its time. Because its publication has been rejected, it was developed to the world of public-key cryptography in the 1980s as oblivious transfer, first by Michael Rabin and then by Shimon Even. It is used in the field of quantum computing. The initial concept of quantum cryptography developed by Bennett and Gilles Brassard was also based on this concept.

Backup

In information technology, a backup, or data backup is a copy of computer data taken and stored elsewhere so that it may be used to restore the original after a data loss event. The verb form, referring to the process of doing so, is "back up", whereas the noun and adjective form is "backup". Backups can be used to recover data after its loss from data deletion or corruption, or to recover data from an earlier time. Backups provide a simple form of IT disaster recovery; however not all backup systems are able to reconstitute a computer system or other complex configuration such as a computer cluster, active directory server, or database server. A backup system contains at least one copy of all data considered worth saving. The data storage requirements can be large. An information repository model may be used to provide structure to this storage. There are different types of data storage devices used for copying backups of data that is already in secondary storage onto archive files. There are also different ways these devices can be arranged to provide geographic dispersion, data security, and portability. Data is selected, extracted, and manipulated for storage. The process can include methods for dealing with live data, including open files, as well as compression, encryption, and de-duplication. Additional techniques apply to enterprise client-server backup. Backup schemes may include dry runs that validate the reliability of the data being backed up. There are limitations and human factors involved in any backup scheme. == Storage == A backup strategy requires an information repository, "a secondary storage space for data" that aggregates backups of data "sources". The repository could be as simple as a list of all backup media (DVDs, etc.) and the dates produced, or could include a computerized index, catalog, or relational database. === 3-2-1 Backup Rule === The backup data needs to be stored, requiring a backup rotation scheme, which is a system of backing up data to computer media that limits the number of backups of different dates retained separately, by appropriate re-use of the data storage media by overwriting of backups no longer needed. The scheme determines how and when each piece of removable storage is used for a backup operation and how long it is retained once it has backup data stored on it. The 3-2-1 rule can aid in the backup process. It states that there should be at least 3 copies of the data, stored on 2 different types of storage media, and one copy should be kept offsite, in a remote location (this can include cloud storage). 2 or more different media should be used to eliminate data loss due to similar reasons (for example, optical discs may tolerate being underwater while LTO tapes may not, and SSDs cannot fail due to head crashes or damaged spindle motors since they do not have any moving parts, unlike hard drives). An offsite copy protects against fire, theft of physical media (such as tapes or discs) and natural disasters like floods and earthquakes. Physically protected hard drives are an alternative to an offsite copy, but they have limitations like only being able to resist fire for a limited period of time, so an offsite copy still remains as the ideal choice. Because there is no perfect storage, many backup experts recommend maintaining a second copy on a local physical device, even if the data is also backed up offsite. === Backup methods === ==== Unstructured ==== An unstructured repository may simply be a stack of tapes, DVD-Rs or external HDDs with minimal information about what was backed up and when. This method is the easiest to implement, but unlikely to achieve a high level of recoverability as it lacks automation. ==== Full only/System imaging ==== A repository using this backup method contains complete source data copies taken at one or more specific points in time. Copying system images, this method is frequently used by computer technicians to record known good configurations. However, imaging is generally more useful as a way of deploying a standard configuration to many systems rather than as a tool for making ongoing backups of diverse systems. ==== Incremental ==== An incremental backup stores data changed since a reference point in time. Duplicate copies of unchanged data are not copied. Typically a full backup of all files is made once or at infrequent intervals, serving as the reference point for an incremental repository. Subsequently, a number of incremental backups are made after successive time periods. Restores begin with the last full backup and then apply the incrementals. Some backup systems can create a synthetic full backup from a series of incrementals, thus providing the equivalent of frequently doing a full backup. When done to modify a single archive file, this speeds restores of recent versions of files. ==== Near-CDP ==== Continuous Data Protection (CDP) refers to a backup that instantly saves a copy of every change made to the data. This allows restoration of data to any point in time and is the most comprehensive and advanced data protection. Near-CDP backup applications—often marketed as "CDP"—automatically take incremental backups at a specific interval, for example every 15 minutes, one hour, or 24 hours. They can therefore only allow restores to an interval boundary. Near-CDP backup applications use journaling and are typically based on periodic "snapshots", read-only copies of the data frozen at a particular point in time. Near-CDP (except for Apple Time Machine) intent-logs every change on the host system, often by saving byte or block-level differences rather than file-level differences. This backup method differs from simple disk mirroring in that it enables a roll-back of the log and thus a restoration of old images of data. Intent-logging allows precautions for the consistency of live data, protecting self-consistent files but requiring applications "be quiesced and made ready for backup." Near-CDP is more practicable for ordinary personal backup applications, as opposed to true CDP, which must be run in conjunction with a virtual machine or equivalent and is therefore generally used in enterprise client-server backups. Software may create copies of individual files such as written documents, multimedia projects, or user preferences, to prevent failed write events caused by power outages, operating system crashes, or exhausted disk space, from causing data loss. A common implementation is an appended ".bak" extension to the file name. ==== Reverse incremental ==== A Reverse incremental backup method stores a recent archive file "mirror" of the source data and a series of differences between the "mirror" in its current state and its previous states. A reverse incremental backup method starts with a non-image full backup. After the full backup is performed, the system periodically synchronizes the full backup with the live copy, while storing the data necessary to reconstruct older versions. This can either be done using hard links—as Apple Time Machine does, or using binary diffs. ==== Differential ==== A differential backup saves only the data that has changed since the last full backup. This means a maximum of two backups from the repository are used to restore the data. However, as time from the last full backup (and thus the accumulated changes in data) increases, so does the time to perform the differential backup. Restoring an entire system requires starting from the most recent full backup and then applying just the last differential backup. A differential backup copies files that have been created or changed since the last full backup, regardless of whether any other differential backups have been made since, whereas an incremental backup copies files that have been created or changed since the most recent backup of any type (full or incremental). Changes in files may be detected through a more recent date/time of last modification file attribute, and/or changes in file size. Other variations of incremental backup include multi-level incrementals and block-level incrementals that compare parts of files instead of just entire files. === Storage media === Regardless of the repository model that is used, the data has to be copied onto an archive file data storage medium. The medium used is also referred to as the type of backup destination. ==== Magnetic tape ==== Magnetic tape was for a long time the most commonly used medium for bulk data storage, backup, archiving, and interchange. It was previously a less expensive option, but this is no longer the case for smaller amounts of data. Tape is a sequential access medium, so the rate of continuously writing or reading data can be very fast. While tape media itself has a low cost per space, tape drives are typically dozens of times as expensive as hard disk drives and optical drives. Tape media are generally rotated on a schedule so at least one set is off-site in case something should happe

Art Recognition

Art Recognition is a Swiss technology company headquartered in Adliswil, within the Zurich metropolitan area, Switzerland. Art Recognition specializes in the application of artificial intelligence (AI) for art authentication and the detection of art forgeries. == Overview == Art Recognition was established in 2019 by Dr. Carina Popovici and Christiane Hoppe-Oehl. Art Recognition employs a combination of machine learning techniques, computer vision algorithms, and deep neural networks to assess the authenticity of artworks. The company's technology undergoes a process of data collection, dataset preparation, and training. === Academic partnerships and grants === Art Recognition has established a relationship with Innosuisse, a Swiss innovation agency, to expand its research and development initiatives. It has also formed a strategic collaboration with Nils Büttner, an art historian and professor at the State Academy of Fine Arts Stuttgart (ABK Stuttgart). === Notable developments === In May 2024, Art Recognition played a key role in identifying counterfeit artworks, including alleged Monets and Renoirs, being sold on eBay. Germann Auction in November 2024 became the first auction house to successfully conduct a sale of artwork authenticated entirely by artificial intelligence. As of January 2025, Art Recognition has appointed art crime expert and Pulitzer Prize finalist Noah Charney as an advisor. === Recognition and debates === The company was featured on the front page of The Wall Street Journal for its involvement in the authentication case of the Flaget Madonna, believed to have been partly painted by Raphael. A broadcast by the Swiss public television SRF covered how the algorithm can be used to detect art forgeries with high accuracy. The technology developed by Art Recognition has been recognized for its role in providing a technology-based art authentication solution, compared to traditional methods. == Controversial cases == Art Recognition's AI algorithm has been applied to several high-profile and controversial artworks, sparking significant interest and debate in the art world. Samson and Delilah at the National Gallery in London: The National Gallery's "Samson and Delilah", traditionally attributed to the artist Rubens, has also been examined using Art Recognition's AI, which has assessed the painting as non-authentic. De Brecy Tondo Madonna. A research team from Bradford University and the University of Nottingham initially attributed the painting to Raphael, employing an AI face recognition software, while the AI developed at Art Recognition returned a negative result. The Bradford group's AI was trained on 49 images, whereas Art Recognition employed a larger dataset of over 100 images. Lucian Freud Painting Controversy: Featured in The New Yorker, a painting attributed to Lucian Freud became a subject of dispute. Art Recognition's AI analysis played a big role in examining the painting's authenticity. Titian at Kunsthaus Zürich: A painting attributed to Titian, housed at Kunsthaus Zürich, has been a topic of debate among art experts. The application of Art Recognition's technology offered a new perspective. Following this debate, Kunsthaus Zürich has announced plans to initiate a comprehensive project aimed at resolving the authenticity questions surrounding the painting. Art Recognition has contributed to the authentication debate surrounding The Polish Rider, a painting traditionally attributed to Rembrandt but subject to scholarly debate.

Ciphertext

In cryptography, ciphertext or cyphertext is the result of encryption performed on plaintext using an algorithm, called a cipher. Ciphertext is also known as encrypted or encoded information because it contains a form of the original plaintext that is unreadable by a human or computer without the proper cipher to decrypt it. This process prevents the loss of sensitive information via hacking. Decryption, the inverse of encryption, is the process of turning ciphertext into readable plaintext. Ciphertext is not to be confused with codetext, because the latter is a result of a code, not a cipher. == Conceptual underpinnings == Let m {\displaystyle m\!} be the plaintext message that Alice wants to secretly transmit to Bob and let E k {\displaystyle E_{k}\!} be the encryption cipher, where k {\displaystyle _{k}\!} is a cryptographic key. Alice must first transform the plaintext into ciphertext, c {\displaystyle c\!} , in order to securely send the message to Bob, as follows: c = E k ( m ) . {\displaystyle c=E_{k}(m).\!} In a symmetric-key system, Bob knows Alice's encryption key. Once the message is encrypted, Alice can safely transmit it to Bob (assuming no one else knows the key). In order to read Alice's message, Bob must decrypt the ciphertext using E k − 1 {\displaystyle {E_{k}}^{-1}\!} which is known as the decryption cipher, D k : {\displaystyle D_{k}:\!} D k ( c ) = D k ( E k ( m ) ) = m . {\displaystyle D_{k}(c)=D_{k}(E_{k}(m))=m.\!} Alternatively, in a non-symmetric key system, everyone, not just Alice and Bob, knows the encryption key; but the decryption key cannot be inferred from the encryption key. Only Bob knows the decryption key D k , {\displaystyle D_{k},} and decryption proceeds as D k ( c ) = m . {\displaystyle D_{k}(c)=m.} == Types of ciphers == The history of cryptography began thousands of years ago. Cryptography uses a variety of different types of encryption. Earlier algorithms were performed by hand and are substantially different from modern algorithms, which are generally executed by a machine. === Historical ciphers === Historical pen and paper ciphers used in the past are sometimes known as classical ciphers. They include: Substitution cipher: the units of plaintext are replaced with ciphertext (e.g., Caesar cipher and one-time pad) Polyalphabetic substitution cipher: a substitution cipher using multiple substitution alphabets (e.g., Vigenère cipher and Enigma machine) Polygraphic substitution cipher: the unit of substitution is a sequence of two or more letters rather than just one (e.g., Playfair cipher) Transposition cipher: the ciphertext is a permutation of the plaintext (e.g., rail fence cipher) Historical ciphers are not generally used as a standalone encryption technique because they are quite easy to crack. Many of the classical ciphers, with the exception of the one-time pad, can be cracked using brute force. === Modern ciphers === Modern ciphers are more secure than classical ciphers and are designed to withstand a wide range of attacks. An attacker should not be able to find the key used in a modern cipher, even if they know any specifics about the plaintext and its corresponding ciphertext. Modern encryption methods can be divided into the following categories: Private-key cryptography (symmetric key algorithm): one shared key is used for encryption and decryption Public-key cryptography (asymmetric key algorithm): two different keys are used for encryption and decryption In a symmetric key algorithm (e.g., DES, AES), the sender and receiver have a shared key established in advance: the sender uses the shared key to perform encryption; the receiver uses the shared key to perform decryption. Symmetric key algorithms can either be block ciphers or stream ciphers. Block ciphers operate on fixed-length groups of bits, called blocks, with an unvarying transformation. Stream ciphers encrypt plaintext digits one at a time on a continuous stream of data, with the transformation of successive digits varying during the encryption process. In an asymmetric key algorithm (e.g., RSA), there are two different keys: a public key and a private key. The public key is published, thereby allowing any sender to perform encryption. The private key is kept secret by the receiver, thereby allowing only the receiver to correctly perform decryption. == Cryptanalysis == Cryptanalysis (also referred to as codebreaking or cracking the code) is the study of applying various methodologies to obtain the meaning of encrypted information, without having access to the cipher required to correctly decrypt the information. This typically involves gaining an understanding of the system design and determining the cipher. Cryptanalysts can follow one or more attack models to crack a cipher, depending upon what information is available and the type of cipher being analyzed. Ciphertext is generally the most easily obtained part of a cryptosystem and therefore is an important part of cryptanalysis. === Attack models === Ciphertext-only: the cryptanalyst has access only to a collection of ciphertexts or code texts. This is the weakest attack model because the cryptanalyst has limited information. Modern ciphers rarely fail under this attack. Known-plaintext: the attacker has a set of ciphertexts to which they know the corresponding plaintext Chosen-plaintext attack: the attacker can obtain the ciphertexts corresponding to an arbitrary set of plaintexts of their own choosing Batch chosen-plaintext attack: where the cryptanalyst chooses all plaintexts before any of them are encrypted. This is often the meaning of an unqualified use of "chosen-plaintext attack". Adaptive chosen-plaintext attack: where the cryptanalyst makes a series of interactive queries, choosing subsequent plaintexts based on the information from the previous encryptions. Chosen-ciphertext attack: the attacker can obtain the plaintexts corresponding to an arbitrary set of ciphertexts of their own choosing Adaptive chosen-ciphertext attack Indifferent chosen-ciphertext attack Related-key attack: similar to a chosen-plaintext attack, except the attacker can obtain ciphertexts encrypted under two different keys. The keys are unknown, but the relationship between them is known (e.g., two keys that differ in the one bit). == Famous ciphertexts == The Babington Plot ciphers The Shugborough inscription The Zimmermann Telegram The Magic Words are Squeamish Ossifrage The cryptogram in "The Gold-Bug" Beale ciphers Kryptos Zodiac Killer ciphers